<p>Most existing reliability-based robust design optimization (RBRDO) models typically integrate target performance and its fluctuations under uncertainties into a single objective, limiting their engineering applications. To overcome this limitation, this paper treats target performance and its fluctuations as independent objectives and proposes a reference vector-guided evolutionary algorithm (RVGEA) for efficiently solving the model. Specifically, the algorithm first optimizes a robust design optimization (RDO) problem derived from the RBRDO problem. Subsequently, it uses a reference vector-guided strategy to screen a set of solutions that perform well in the objective space from the RDO optimization results. These solutions are used to explore the objective space, enabling rapid convergence to the Pareto front (PF) of the RBRDO problem. After verifying accuracy through comparisons with other algorithms on two engineering cases, RVGEA is applied to the design of a gearbox for offshore wind turbines to validate its practicality.</p>