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Reducing delays in the metro transit system improves passenger satisfaction and the operational efficiency of the system. However, current delay-recovery strategies tend to reduce delays rather than the operational costs of the metro company. We propose optimized delay-recovery strategies to reduce the delay and increase energy efficiency simultaneously in this study. We classified delays in historical data according to the relationship between the delay time and the following headway, and we present optimized delay-recovery strategies for different initial delay conditions. A multi-objective optimization model was used to minimize the cumulative delay time (CDT) and energy consumption. We added weight factors to the objective functions to reflect the preferences of decision-makers regarding delay recovery and energy savings. We applied the non-dominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model and verified its performance using a case study of the Chengdu Metro. The results showed that the proposed model exhibited excellent performance regarding delay recovery and energy savings. • Optimized strategies were used to reduce the delay and increase the energy efficiency of metro lines. • A multi-objective programming model was proposed to deal with different initial delay scenarios. • The NSGA-II algorithm was used to solve the optimization problem. • Proposed model were verified using a case study of the Chengdu Metro in China. [ABSTRACT FROM AUTHOR]