The over all value of a product is typically determined by its performance with respect to multiple measure. As such, the product design task will be simplified if all these performance measures were optimized simultaneously. Another significant factor that determines product quality is its sensitivity to external or uncontrollable variation. to effectively address these issues in product design, this paper presents a novel robust multiple criteria optimization (RMCO) approach that integrates multi-objective optimization concepts with statistical robust design techniques. In this approach, Pareto-optimal robust design solution sets are obtained with the aid of design of experiment set-ups, that utilize ANOVA results to quantify relative dominance and significance of design factors. Application of this method to engineering design problems is illustrated with the aid of two case studies including a mechanism dimensional synthesis problem.