With the rapid development of computer technology and UAV countermeasure technology, single UAV combat has more and more limitations due to the actual battlefield environment and the complexity of the task. In view of the diversified combat mission requirements in the modern battlefield, multi-UAV cooperative combat has greater advantages, and is one of the focuses of current research. Multi-uav mission planning mainly solves how to efficiently assign multiple tasks to a group of UAVs and formulate corresponding flight paths to meet the task requirements while optimizing performance indicators (such as minimizing total flight time and maximizing task completion rate, etc.). This paper presents a multi-UAV mission planning method based on gray Wolf optimization algorithm. Grey Wolf Optimizer (GWO) is an emerging swarm intelligence optimization algorithm inspired by the social hierarchy and hunting behavior of grey wolves. The algorithm performs well in global search ability and convergence speed, and is especially suitable for solving large-scale optimization problems. In this paper, we apply GWO algorithm to multi-UAV mission planning problem to achieve dual goals of task assignment and path optimization. Taking the shortest distance as the objective function, this paper establishes the task assignment and path planning model, and proposes a multi-UAV multi-target task planning algorithm based on gray Wolf optimization algorithm. In order to verify the actual effect and stability of the algorithm, three mathematical simulation calculations are carried out on the same set of data using the algorithm. The total path length of the three experiments is 4209.33, 4305.46 and 4463.73, the number of algorithm convergence iterations corresponding to the experiments is 335, 228 and 312. In order to further verify the effectiveness of the algorithm, this paper also uses the Pelican intelligent optimization algorithm(POA) for comparison calculation, and the results show that the Gray Wolf optimization algorithm has obvious advantages in convergence speed and optimization results, and can more efficiently realize the shortest distance multi-UAV mission planning.