Mission planning is one of key technologies for Unmanned Aerial Vehicle (UAV) cooperative combat. For the task of suppressing the enemy's aerial-defense firepower, we established a mission planning model for multi-UAV cooperative attacking multiple ground targets by taking terrain and threat distribution, firepower resource needed to destroy the targets, and damage probability of UAVs into consideration. A parallel Genetic Algorithm and Particle Swarm Optimization (GAPSO) algorithm was proposed to resolve this multi-UAV cooperative mission planning problem. Simulation example verified the rationality of the mission planning model. The comparison between parallel GAPSO algorithm and traditional GAPSO algorithm showed that the parallel algorithm has better convergence performance and could avoid trapping in local optimum.