Abstract： This paper presented a fuzzy logic control strategy for a parallel HEV equipped with battery/ultra capacitor based hybrid energy storage system. By combining the population evolution feature of genetic algorithm and the randomicity and ergodicity of chaos sequence, the chaotic initialization, disturbance and local search operators were introduced into non-donminated sorting genetic algorithm-II(NSGA-II) to construct a novel multichaotic operators NSGA-II (MCO-NSGA-II). MCO-NSGA-II was adopted to optimize the fuzzy control strategy for improving the fuel economy and emission performance of the target HEV. The results demonstrate that chaotic initialization, disturbance operators can improve the searching ability of NSGA-II and increase the diversity of the solutions. The chaotic local search operator can further improve the local searching ability to obtain better pareto solutions. By adopting MCO-NSGA-II, the fuel consumption of HEV under ECE driving cycle is reduced by 11.8% while the HC, CO and NOx emissions of HEV are decreased by 7.72%, 15.72% and 11.77%.