Abstract: This study develops a coverage path planning method for multi-UAV maritime search and rescue (MSAR) missions under dynamic ocean conditions and time-critical constraints, aiming to balance search efficiency and resource allocation. Firstly, a grid-based regional decomposition approach is adopted to discretize complex maritime environments into visual planning cells, while a Gaussian Mixture Model (GMM) is employed to construct a prior target-drift distribution and generate a probabilistic map for path guidance. Secondly, for multi-UAV coverage planning, an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is developed to jointly optimize task allocation, path safety, coverage of high-priority areas, and energy consumption control. Thirdly, to enhance global search capability and convergence performance, the algorithm incorporates a Sigmoid-based adaptive inertia weight strategy, a two-level elite-guided crossover strategy, and a constraint-penalty mechanism. Finally, three UAVs were deployed to conduct simulation tests over MSAR regions of various shapes. Results show that, compared with classical baseline algorithms, the proposed method achieves up to 30.27% improvement in cumulative detection probability, 82.5% improvement in workload balance, and 1.28% reduction in total path length within the first 50 steps, demonstrating its effectiveness and practicality for improving MSAR efficiency and coordination.