The state-of-the-art evolutionary algorithms (EAs), developed to solve constrained multi/many-objective optimization problems (M/MaOPs), mostly deal with deterministic design variables causing no uncertainty in their implementation. However, from a practical point of view, it is imperative to consider unavoidable uncertainties in implementing design variables and parameters. In the presence of hard constraints, a slight change in one or more variables may cause a feasible optimal solution to become infeasible upon implementation and result in a failure during operation. The literature suggests reliability-based techniques for solving such M/MaOPs to obtain a Reliable Front (ReF), rather than a Pareto-optimal front (PF). A ReF is usually either a part of the PF or a completely different set of trade-off solutions dominated by the deterministic PF. However, in the presence of decision-making, computing the complete ReF may not be necessary, as the focus would be to locate only the preferred part of the ReF, dictated by the objective preference information provided by a decision-maker (DM). The proposed approach incorporates DM’s preferences and variable uncertainty information a priori. The proposed Reliability-based Multi-criteria Decision-making (ReMCDM) approach uses the hybrid mean value (HMV) method for constrained handling under variable uncertainties and R-NSGA-III for preference incorporation by DMs to conduct MCDM. Results obtained by the proposed method, implemented on several benchmark and real-world engineering examples, encourage future EMO research combining constraint handling, uncertainty in decision variables, and preference incorporation.