The work aims to put focuses on optimizing air cargo loading efficiency, cost, and safety by finding optimal solutions for multi-objective container loading plans. A practical multi-objective model was established for aircraft container loading, focusing on volumetric efficiency, improving payload efficiency, and minimizing center of gravity shifts, while comprehensively considering seven types of constraints including weight limitations and volume restrictions. A hybrid intelligent algorithm was devised for the resolution of the given model. Initially, a tripartite spatial segmentation methodology was integrated with an enhanced Genetic Algorithm (GA) to produce an incipient loading configuration. Subsequently, the algorithm underwent extensive refinement through the application of a Large-scale Multi-objective Evolutionary Algorithm (LSMOEA), to ultimately yield the definitive loading scheme upon holistic consideration of the values associated with multiple objective functions. Validation was conducted with BR samples and authentic freight datasets. Results showed a mean packing density surpassing 93% for various cargo types in BR cases, with volumetric efficiency and payload utilization averaging at 90.56% and 76.40% in real shipments. Additionally, the mean center-of-gravity offset remained under 5 cm. The research affirms the algorithm's notable versatility in addressing multi-objective air container loading, significantly enhancing cargo transportation efficiency and safeguarding transit security.