Traditional methods often exhibit low efficiency in addressing multi-objective and multi-constraint optimization problems, failing to meet the requirements of dynamic and complex environments. In this case, a hybrid cooperative co-evolution algorithm was proposed based on cooperative co-evolution mechanisms, zebra optimization algorithms, and differential game theory. A phased optimization strategy was adopted to dynamically and adaptively optimize trajectories and strategies, while a multi-population co-evolution mechanism was introduced to enhance global exploration capability and local convergence performance. Differential game theory was integrated to improve the stability and reliability of game strategies. Simulation results demonstrate that this method significantly improves mission completion efficiency under multi-constraint conditions. It effectively balances dynamic strategy adjustments for both pursuers and evaders, providing an effective solution for satellite pursuit-evasion games in space-based target reconnaissance and surveillance missions.