The problem of robust design is treated as a multi-objective optimization issue in which the performance mean and variation are optimized and minimized respectively, while maintaining the feasibility of design constraints under uncertainty. To effectively address this issue in robust design, this paper presents a novel robust optimization approach which integrates multi-objective optimization concepts with Taguchi’s crossed arrays techniques. In this approach, Pareto-optimal robust design solution sets are obtained with the aid of design of experiment set-ups, which utilize the results of Analysis of Variance to quantify relative dominance and significance of design variables. A beam design problem is used to illustrate the effectiveness of the proposed approach. The problem of robust design is treated as a multi-objective optimization issue in which the performance mean and variation are optimized and minimized respectively, while maintaining the feasibility of design constraints under uncertainty. To effectively address this issue in robust design, this paper presents a Novel robust optimization approach which integrates multi-objective optimization concepts with Taguchi’s crossed arrays techniques. In this approach, Pareto-optimal robust design solution sets are obtained with the aid of design of experiment set-ups, which utilizes the results of Analysis of Variance to quantify relative dominance and significance of design variables. A beam design problem is used to illustrate the effectiveness of the proposed approach.