A new optimization model, integrating maximum a posteriori(MAP) estimation of the Bayesian approach and the linear weighting method for multi-objectives in the framework of Gaussian process regression(GPR) modeling, is proposed to solve the problem of robust parameter design for the uncertainty of response. Firstly, optimal hyperparameters are obtained by using a MAP method, and the Gaussian regression model is constructed. Then, a multi-objective optimization model is constructed by using the linear weighting criterion. Finally, the optimal parameter solution is obtained by cluster analysis. The proposed method considers the effect of output response uncertainty on the optimization results and balances the relationship between the optimal factor level and the multivariate quality characteristics．The effectiveness of the proposed method is verified through a practical industrial example combined with a simulation example. The results show that the proposed method can give good consideration to the optimal and the robust of response, so as to achieve the robust parameter design.