Experimenting with limited resources often means that we are trying to get more out of a single experiment and balance competing goals. Selecting a best response surface design when simultaneously optimizing multiple criteria requires carefully choosing measures and scales of different design criteria and then balancing the trade-offs between the criteria. This paper illustrates a decision-making process using a Pareto frontier to identify good design candidates and a Utopia point approach for selection of an optimal design based on several competing criteria. The Pareto approach shows substantial improvement over the classic desirability function method by offering the user greater flexibility in quantifying the robustness of designs to weight specifications and the sensitivity of the solutions to different choices of weights, scales, and metrics. Graphical methods are used for summarizing and extracting useful information for improved decision-making. Copyright © 2012 John Wiley & Sons, Ltd.