<p>Engineering systems often encounter inherent uncertain parameters during their design phase and throughout their service life. This leads systems to deviate from their intended performance, which results in catastrophic failures, including fatalities and significant capital loss. To address the increasing need for safer and more resilient systems that are less susceptible to uncertainties, this paper presents a novel framework aimed at ensuring both reliability and robustness. The methodology introduces a two-stage decoupled probabilistic framework for reliability-based robust design optimization (RBRDO). Handling both stochastic constraints (failure probability) and a stochastic objective function (robustness measures) simultaneously in RBRDO has proven to be significantly challenging, often aggravating the accuracy and efficiency of the design algorithms in the literature. To obviate these difficulties, the proposed framework, utilizing a simulation-based approach, is the first-of-its-kind. It decouples stochastic constraints from the stochastic objective functions within the optimization loop by first iteratively identifying a feasible design space that meets reliability constraints. Subsequently, it optimizes the robustness measures—specifically, the mean and variance within the identified feasible design space. Here, “<i>augmented formulation</i>” is defined for both robustness measures (mean and variance) and reliability constraints. Voronoi tessellation is used for design space exploration and exploitation, while the optimization is carried out using a simulation-based stochastic optimization method, namely “<i>Improved Stochastic Subset Optimization (iSSO)</i>.<i>”</i> Benchmark case studies investigated complex linear and nonlinear four- and ten-bar trusses, a transmission tower truss structure, and a base-isolated structure. The proposed framework is observed to have higher accuracy and lower computational demand in comparison to existing literature.</p>