The optimization of aircraft loading problems are critical for operational efficiency and safety in the aviation industry. Therefore, how to improve the convergence speed and solution quality for complex multi-objective optimization problems in aircraft loading raises widespread concerns. Existing solutions are monolithic and cannot optimize multiple performance simultaneously. In this paper, we propose a Hybrid Optimization Algorithm for Multi-objective Problems with Complex Constraints (HybridMOCC) to solve the aircraft loading problem. Specifically, we present a comprehensive analysis of the proposed HybridMOCC algorithm, detailing its theoretical foundations and operational intricacies. Through rigorous experimental setups using aircraft loading, the algorithm’s performance is juxtaposed against several state-of-the-art optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and others. The experimental results show that the HybridMOCC’s superior performance in terms of solution quality, convergence speed, and consistency. Furthermore, the research delves into the real-world challenges and limitations of implementing the HybridMOCC algorithm in dynamic and complex aviation transportation environments. Potential future directions, including adaptive parameter tuning, hybrid approaches, and real-time optimization, are also explored.