This article proposes an approach to select attractive system designs out of a solution set for a multi-objective optimization problem concerning the robustness to parameter variations. Various uncertainties, such as the system modelling error, change in operational conditions and increases/decreases in the cost elements, are considered. Three different sensitivity-based down-selection methods—the sensitivity Pareto front, the sensitivity-threshold combination Pareto front and the sensitivity-effect weighted sum—are introduced. The proposed methods are compared with the robust optimization approach in terms of performance and computational cost. Engineering design case studies demonstrate the validity and potential applicability of the proposed approach.