Considering multiple constraints such as track length and external threats, this study provides a collaborative path planning approach using enhanced multi-population gray wolf optimization (MPGWO) algorithm, which fully improves the speed and optimality of cooperative flight of numerous UAVs. In proposed algorithm, variable length chromosome coding is used to describe the collaborative trajectories of multiple UAVs. We suggested a trajectory population initialization approach based on greedy strategy and nonlinearly altered the convergence factor to address the weak search and development abilities in the classical gray wolf algorithm. In addition, the dynamic weighting rule in the position update strategy is introduced to improve the flexibility of search strategy updates. Theoretically, the fully enhanced MPGWO promotes the optimum performance of the solution while simultaneously accelerating convergence. Finally, we verified the potency and excellence of the improved multi-population gray wolf algorithm based on the MATLAB simulation platform.