Modern warfare is characterized by high intensity, fast pace and multi-dimensional confrontation. The delivery of battlefield materials puts forward higher requirements for time constraints and resource allocation. This study focuses on the mission planning problem of unmanned transport aircraft with time window constraints and material demand limitations, constructs an optimization model based on the Whale Optimization Algorithm (WOA), and verifies the algorithm's effectiveness through mathematical simulation. The Whale Optimization Algorithm (WOA) can quickly optimize in complex constraint Spaces by simulating the predatory behavior of humpback whales: its global search ability can effectively avoid local optimal solutions, while the local development mechanism can precisely optimize path details. Thus, under the premise of meeting the constraints of time Windows and payload capacity, it can dynamically balance the cooperative relationship between path length and the number of unmanned aerial vehicle (UAV) sorbets. The simulation results show that the path cost calculated in the three iterations of this algorithm is significantly lower than that of the conventional mathematical method (the result of the conventional method is 774.3, and the convergence efficiency is stable, verifying its effectiveness and superiority in handling such multi-constraint material delivery tasks. This research expands the application scenarios of intelligent algorithms in the field of military operations research and provides a new technical path for improving the efficiency of battlefield material delivery.