This paper deals with two major issues critical to the development and implementation of a decision-based robust design, namely, representation of design performance under conditions of uncertainty and the development of a robust design decision model. Specifically, this paper presents a computationally efficient procedure for accurate estimation of performance variance using a novel Surround Point Method (SPM) and discusses its incorporation into a decision-based robust design framework. Results indicate that by mimicking effects from Monte-Carlo Simulation (MCS), SPM-based uncertainty estimation method appears to offer the best promise in achieving an optimal balance between computational complexity and design-scenario independence. It can be expected to be a viable and applicable probability estimation tool in generic engineering design, and particularly useful in highly nonlinear configuration design with many design variables. Furthermore, to explicitly incorporate robustness criteria, this paper introduces the concept of design evaluation level as a means for decision-making in an evolving design process. Using this concept, this paper introduces a robust decision-based design methodology that can methodically handle multiple performance attributes, system constraints, and robustness issues in engineering design. These issues are discussed in the context of engineering design decision-making with the aid of a simple case study and the results are discussed.