Provided is a multi-objective optimization method based on a Gaussian process simultaneous MIMO model. According to the method, samples are obtained in an experiment design method, a mapping relation between design variables and responses to be studied is approximately set by utilizing the Gaussian process simultaneous MIMO model, multi-objective optimization is carried out on the Gaussian process simultaneous MIMO model in a Gaussian variation hybrid genetic algorithm, an ant colony algorithm and the like, then Pareto front related to design variable combinations is obtained, furthermore, distribution quality of solution sets in the Pareto front is judged, finally, one design variable combination in the Pareto front is selected according to specific demands for high-accuracy analysis and solution, and physical experiments are carried out when obtained results are satisfactory. According to the multi-objective optimization, experiment design, the high-accuracy analysis and solution, an agent model technology and an optimization algorithm are integrated and applied to the optimization design, therefore, time consumption and computation cost of the optimization design are reduced greatly, and work efficiency is greatly improved.