The natural cutoff frequency parameter of the filter in the wash-out algorithm is the main factor affecting the wash-out effect, and then affect the motion fidelity of the flight simulator. In order to obtain the optimal parameters of the natural cut-off frequency of the filter, an improved multi-objective grey wolf algorithm is put forward to find the most suitable filter parameters. The Logistic-tent mapping is used for initialization to improve the diversity of the population. The belief of the differential evolution algorithm is combined to the multi-objective grey wolf algorithm for iterative optimization. The nonlinear control parameters and the introduction of the inertia weight strategy are used to update the population position, which effectively balances the algorithm"s global search and sectional development abilities. The objective function is built by using three evaluation indicators of the wash-out algorithm, and use the fuzzy membership function to get the best solution. The simulation and experimental results demonstrate that the improved multi-objective grey wolf algorithm reduces the 1.23s phase delay and corrects the sensory peak compared with the classic wash-out algorithm, while saving 18.5% of the working space of the motion platform, and the wash-out effect is more ideal.