In order to improve the static characteristics of disc porous aerostatic thrust bearings,mind evolutionary algorithm (MEA) was used to optimize the back propagation (BP) neural network to create the prediction model of the static characteristics of disc porous aerostatic thrust bearings.The high precision prediction of static bearing capacity,static stiffness and gas consumption was completed,and the prediction errors were less than 2%,5% and 5% respectively.Based on this model,to maximize static bearing capacity and static stiffness and minimize gas consumption as optimization objectives,the multi-objective optimization of the bearing porosity parameter combination was carried out by using genetic algorithm,and the rapid optimization design of bearing parameters was realized.The optimization results show that the static bearing capacity increases by 64.53%,the static stiffness increases by 31.93%,and the gas consumption decreases by 56.52%.