In the Plug-in hybrid electric bus, the power train parameter and control strategy significantly affect the economy and dynamic performance. Thus, the simultaneous optimization of power train parameter and control strategy is designed for the trade-off between the dynamic and economic performance. Depending on the parallel electric auxiliary control strategy in a plug-in hybrid electric bus, a vehicle dynamic simulation model is built with the software AVL Cruise. Aiming at the minimization of equivalent gas consumption and acceleration time from 0 to 50kmph, the gear ratio, final drive ratio, gear shifting strategy and control strategy are chosen as optimal variable, which significantly impact power performance and fuel economy. The driving performance and the driving range with full battery are considered as constraints. Based on the software Isight, multi-objective optimization model is built by adopting non-dominated sorting genetic optimization algorithm (NSGA-II). NSGA-II adopts non-dominated sort and congestion comparison operator to make the front close the Pareto, as well as a better diversity. Simultaneously, its elite strategy can improve its arithmetic speed and robustness. The optimization model is performed over the Chinese Typical Urban Driving Cycle, and the Pareto optimal solution is obtained. Finally compared with the original Plug-in Hybrid Electric Bus, equivalent gas consumption is decreased by 4.1% and acceleration time is decreased by 3.4% based on the vehicle dynamic performance.