Efficient management of aircraft and crew recovery system is crucial for cost savings and improving the satisfaction, which are related to the airline's reputation. However, most existing work considers only one objective of minimizing costs or maximizing satisfaction. In this study, we propose a new integrated multi-objective recovery system that takes both cost and satisfaction into account simultaneously. To better capture crew satisfaction in the event of airport closure, a bidding mechanism for early off-duty task is designed. To overcome the experience-dependent and labour-consuming problems associated with current manual or mathematical recoveries, we develop an intelligent optimizer based on multi-swarm and MOPSO frameworks, termed adaptive seeking and tracking multi-objective particle swarm optimization algorithm (ASTMOPSO). Specifically, during the evolutionary process, the sub-swarm size undergoes adaptive internal transfer while executing more efficient evolutionary strategies to approach the global Pareto front. Additionally, five ad-hoc repair procedures are designed to ensure feasibility for our aircraft and crew recovery system. The ASTMOPSO is applied to real-world instances from Shenzhen Airlines with different sizes. Experimental results demonstrate the statistical superiority of our method over other popular peer algorithms. And the infeasible solution repair procedures significantly improve the feasibility rate by at least 40%, particularly for large-scale instances.