With the increasing demand for space security, the significance of satellite pursuit-evasion games in the field of space security has become increasingly prominent. Aiming to address the issue that traditional methods exhibit low efficiency in multi-objective and multi-constraint optimization and fail to meet the requirements of dynamic complex environments, a hybrid co-evolutionary algorithm was proposed based on co-evolutionary mechanisms, Zebra Optimization Algorithm, and differential game theory. Through phased optimization strategies, dynamic adaptive optimization of trajectories and strategies was achieved, while a multi-population co-evolutionary mechanism was introduced to enhance the algorithm's global exploration capability and local convergence performance. Combined with differential game theory, the stability and reliability of game strategies were improved. Simulation experiment results demonstrate that the proposed method significantly enhances mission completion efficiency under multi-constraint conditions, while simultaneously accommodating dynamic strategy adjustments by both pursuers and evaders, providing an effective solution for satellite pursuit-evasion games in space-based target reconnaissance and surveillance missions.