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Target tracking has become an important research topic, which applies in communication navigation, computer video and auto control fields. Aiming at the low tracking reliability of the existing marginal particle filter (MPF) algorithm, a target tracking method based on optimized adaptive genetic algorithm (AGA) and auxiliary MPF is presented. According to reduced dimension in state space, a novel auxiliary MPF is derived firstly, where the target motion state is organically separated into both linear component and nonlinear component. In view of linear component, the Kalman filter is utilized. As for nonlinear component, the explicit proposal probability distribution function is achieved based on auxiliary filtering variable. Besides, an optimized AGA is presented to adjust both crossover probability and mutation probability for drawing robust particles that can approximate target motion state. The example results indicate that the proposed method can effectively track normal targets with accurate estimation.