This paper proposes a Multi-Objective Cross-Attention (MOCA) reinforcement learning model based on PPO, tailored for multi-UAV task allocation. By leveraging a multi-head cross-attention mechanism, it effectively captures complex dependencies between UAVs and tasks, optimizing multiple objectives such as minimizing the longest flight time, reducing total travel distance, and balancing task load. MOCA overcomes limitations of traditional heuristic and optimization methods, including poor adaptability to dynamic environments and difficulty handling complex constraints. Extensive simulations show that MOCA outperforms Particle Swarm Optimization, greedy algorithms, and Multi-Agent PPO in flight time control, path efficiency, and load balancing. Ablation studies confirm the crucial role of the multi-channel encoder and cross-attention modules, demonstrating MOCA’s robustness and potential for real-world multi-UAV coordination and mission planning.