Selecting the proper process conditions for the injection-molding process is treated as a multiobjective optimization problem, where different objectives, such as minimizing the injection pressure, volumetric shrinkage/warpage, or cycle time, present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an integrated simulation-based optimization system that incorporates the design of computer experiments, Gaussian process (GP) for regression, multiobjective genetic algorithm (MOGA), and levels of adjacency to adaptively and automatically search for the Pareto-optimal solutions for different objectives. Since the GP approach can provide both the predictions and the estimations of the predictions simultaneously, a nondominated sorting procedure on the predicted variances at each iteration step is performed to intelligently select extra samples that can be used as additional training samples to improve the GP surrogate models. At the same time, user-defined adjacency constraint percentages are employed for evaluating the convergence of iteration. The illustrative applications in this paper show that the proposed optimization system can help mold designers to efficiently and effectively identify optimal process conditions. (C) 2007 Wiley Periodicals, Inc.