Aiming at the problem that traditional grey wolf algorithm is prone to local optimality in 3D path planning, an improved grey wolf algorithm is proposed in this paper. Firstly, the environment of the three-dimensional threat region is modeled, and the total cost function of UAV flight is specified under the constraint conditions. Secondly, chaotic sequences and quasi-reverse learning strategies were added to the initialization of grey wolf population, which increased the diversity of species and the search scope of unknown domain, and improved the adaptive weight factors to update individual positions, thus speeding up the convergence speed. Finally, in order to avoid falling into local optimization, particle swarm optimization algorithm is introduced to balance global development and local convergence. The experimental results show that compared with the other three typical path planning algorithms, the improved gray wolf algorithm can find a safe and feasible path, and has a stable optimization ability.