In large cities with heavily congested metro lines, unexpected disturbances often occur, which may cause severe delay of multiple trains, blockage of partial lines, and reduction of passenger service. Metro dispatchers have taken a practical strategy of rescheduling the timetable and adding several backup trains in storage tracks to alleviate waiting passengers from crowding the platforms and recover from such disruptions. In this study, we first develop a mixed integer programming model to determine the optimal train rescheduling plan with considerations of in-service and backup trains. The aim of train rescheduling is to frequently dispatch trains to evacuate delayed passengers after the disruption. Given the nonlinearity of the model, several linearization techniques are adapted to reformulate the model into an equivalent linear model that can be easily handled by the optimization software. Numerical experiments are implemented to verify the effectiveness of the proposed train rescheduling approach.