This paper investigates the real-time joint optimization problem of train rescheduling and train trajectory optimization under disruptions. A two-level optimization method is proposed to solve the problem. The upper level constructs a mixed-integer programming model to address the train platforming and rescheduling problem (TPRP) during a major disruption. The objective is to minimize the deviations from the original timetable and platform schedule, the costs of train cancellations and stopping plan changes. This proposed model can accommodate diverse interlocking mechanisms in rail passenger station yards. In the lower level, a multi-phase optimal control (MPOC) model is established to address the single train trajectory optimization problem (TTOP) with the aim of energy-saving. Under the rolling optimization framework, information is transferred between the upper and lower models. Specifically, the upper model is solved by a solver and supplies the train running times and train orders to the lower-level model. Subsequently, the lower-level model is solved using the pseudospectral method. If the lower-level solution does not satisfy the safety headway at a segment, the updated information (e.g., the minimum safety headway of two trains at leaving points) is fed back to the upper level. The upper model is then re-optimized to ensure that the lower-level solution complies with the safety headway constraints. Numerical experiments are conducted based on a line with five stations, and the results demonstrate the effectiveness of the model and algorithm.