The effects of axial profile parameters on the main bearing performance of the engine were investigated through the numerical method based on elasto-hydrodynamic lubrication theory, average flow, and asperity contact model. Results show that quadratic profile significantly improves the bearing performance, and the influence of profile varies with its width-to-height ratio. The performance is most improved when the ratio is between 0.8 and 2. An artificial neural network fitting model was developed to predict bearing performance, and multiobjective optimum analyses were performed using genetic algorithm and particle swarm optimization. The optimization goals are average peak asperity contact pressure and average total friction loss. The obtained Pareto front roughly includes three groups, and solutions in group 1 achieve a balance of the two goals, with a width-to-height ratio of 1.5–2. Finally, bearing friction tests were conducted on four profiled bearings to verify the simulation model and optimization results.