A Lift Planning System (LPS) is an imperative component for optimal and safe autonomous crane lifting. Two crucial issues concerning the dynamic nature of the crane system and the construction environment are lifting path re-planning and lifting trajectory planning. The path re-planning scenario involves making decisions on re-generating portions of an already planned lifting path in the presence of dynamic objects in the construction scene and strategizing the implementation of that decision. On the other hand, the trajectory planning problem calls for anti-swing optimal trajectory generation of planned lifting paths to supply reference inputs to the controller of underactuated tower cranes. The primary focus of the current research work is to develop a path re-planning module and a trajectory planning module for an LPS possessing the superior ability to plan collision-free optimal lifting paths in complex construction environments in near real-time. The application is exclusive to tower crane operations in residential or non-residential building construction. Building environments from Building Information Modeling (BIM) systems are utilized in the LPS. Dynamic objects in the scene, considered obstacles, are classified according to their effect on the planned lifting path. The obstacles are updated to the original SDM of the environment via a Single-level Depth Map (SDM) integration technique to portray the dynamic nature of the construction scene. Following the obstacle modelling, a re-planning module constituting a Decision Support System (DSS) and a Path Re-planner (PRP) are prepared. A novel re-planning decision-making algorithm using multi-level Oriented Bounding Boxes (OBBs) is formulated for the DSS. A path re-planning strategy via updating the start configuration for the local path is devised for the PRP. Experiments with scaled real-world models of a building and a specific tower crane show excellent decision accuracy and near real-time re-planning with high optimality. Reducing the unactuated payload motion is a crucial issue for underactuated tower cranes with spherical pendulum dynamics. Moreover, the planned trajectory should be optimal in terms of time and energy to facilitate optimum output at the expense of optimum effort. An offline anti-swing multi-objective trajectory planner is developed in this research for autonomous tower cranes where the hoist-cable and the payload together act as the pendulum. Analyzing the nonlinear dynamics of all the fundamental crane operations, the trajectory planning problems are converted to constrained Multi-Objective Trajectory Optimization Problems (MOTOPs) by parameterizing the corresponding flat outputs via suitably selected Bézier curves. A well-established Multi-Objective Evolutionary Algorithm (MOEA), namely Generalized Differential Evolution 3 (GDE3), is selected as the optimizer, through a detailed comparison with another MOEA, i.e. Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II). The crane operation trajectories are computed via the corresponding planned flat output trajectories. Experiments simulating all lifting operations validate the effectiveness and reliability of the advocated strategy. In scenarios involving comparable masses of hook and payload or long rig-cable, both the hook and the payload exhibit spherical-pendulum behaviour, making the trajectory planning problem highly challenging. The aforementioned offline trajectory planner is equipped with the additional flat output constructor for autonomous double-pendulum tower cranes to deal with the double-swing. The MOTOPs for the trolley/slew operations are formulated through consideration of mechanical and safety constraints, via Bézier curve parameterization. The conventional GDE3 optimizer is improved by integrating a new population initialization strategy, incorporating various concepts of computational opposition. Statistical results of experimental studies with trolley and slew operations verify the superiority of the new MOEA, namely Collective Oppositional GDE3 (CO-GDE3), over the standard GDE3, in terms of convergence and reliability. The simulated trajectory results demonstrate that the proposed planner can produce time-energy optimal trajectories, keeping all the state variables within their respective limits, and reducing the residual payload swing to zero.