Abstract: In response to multiple unmanned aerial vehicles (multi-UAV) cooperative reconnaissance and strike mission planning process in dynamic and uncertain battlefield environments, characterized by information uncertainty, multiple constraints, and strong coupling of trajectories across multiple areas and targets, combined with Dubins trajectory planning algorithm, a grey wolf optimization algorithm incorporating multiple improvement strategies (IMISGWO) was proposed. Firstly, to address the uncertainty in UAV cruise speed and reconnaissance-strike task disappearance time caused by dynamic environments, a reconnaissance-strike integrated mission planning mathematical model maximizing task benefits was established based on credibility theory; Secondly, to achieve rapid problem-solving, the strategies of uniform distribution of initial solution, adjustment of individual communication mechanism, dynamic weight updating, and jumping out of the local optimization were designed to improve the solution search capability of the algorithm; Finally, a typical simulation scenario of multi-UAV reconnaissance-strike integrated tasks was constructed, and the feasibility and effectiveness of the algorithm were verified through numerical simulation and hardware-in-loop (HIL) technique. The simulation results demonstrate that the proposed algorithm can efficiently generate multi-UAV task execution sequences and flight trajectories that satisfy the flight performance constraints of multi-UAV when solving the multi-UAV reconnaissance and strike mission planning problem with coupled trajectories under uncertain environments, and it can be applied to solving this kind of problem in situations where the complexity increases due to the growing number of multi-UAV.