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[Objective] Maintenance strategy is the key factor influencing the lifetime service performance and maintenance cost of metro tunnel. Therefore, it is essential to develop more suitable optimization algorithms for maintenance strategies. [Method] A tunnel service performance degradation model is established based on Gamma process, and parametric assumptions are made for inspection schedule and maintenance activities. The improved particle swarm optimization (PSO) algorithm is proposed to solve the stochastic problem in maintenance strategy optimization mathematical model, and its effectiveness is verified through a comparison with gridded enumeration algorithm. The impact of different preventive maintenance thresholds and initial inspection time intervals on maintenance costs is analyzed. [Result & Conclusion] The improved PSO algorithm enhances the computational efficiency of optimizing metro tunnel maintenance strategies. Compared to the initial inspection time interval, the lifetime maintenance cost of metro tunnel is more sensitive to preventive maintenance threshold.