With the increasing number of aviation weapon models, the quantity and complexity of aviation weapon testing tasks have significantly risen. Traditional testing methods, characterized by long testing times and low resource utilization, fail to meet the current demands of aviation weapon testing. To address this, a parallel testing method for aviation weapons based on an improved multi-objective grey wolf optimization algorithm is proposed. This method establishes two optimization objectives: minimizing total test execution time and balancing resource utilization. After initializing the population, two subpopulations are generated based on the different optimization objectives, each assigned distinct fitness functions. Each subpopulation evolves using the improved grey wolf algorithm, with a neighborhood search strategy incorporated after each evolution to enhance local search capabilities. Simulation experiments demonstrate that, under identical conditions, the proposed algorithm improves testing efficiency by 70.9% compared to serial testing methods. Compared to single-objective parallel scheduling algorithms, such as the basic grey wolf optimization algorithm and genetic algorithm, the average resource utilization rate is improved by 10.23% and 8.4%, respectively. These results validate the effectiveness and feasibility of the algorithm, demonstrating its potential for practical engineering applications.