The traditional Mayfly algorithm for UAV path planning has the problems of poor stability, low accuracy, slow convergence speed and local optimization.To solve the problems, a multi-strategy improved Mayfly algorithm applied to UAV path planning is proposed.Firstly, the cost function model and the environment model are established, and the UAV path planning problem is transformed into an optimization problem that meets the feasible route requirements and the route safety constraints.Secondly, Particle Swarm Optimization (PSO) is initialized based on Lévy flight principle, and the traditional Mayfly algorithm is improved by using adaptive t-distribution and Pareto-based elite retention strategy.Finally, the validity of the proposed algorithm is verified by simulation experiment.The simulation results show that the performance of the improved Mayfly algorithm is better than that of the traditional Mayfly algorithm and the particle swarm optimization, and the quality of the planned trajectory is higher.