The conceptual design of complex systems presents significant challenges due to their large scale, multidisciplinary nature, and dependence on technical, economic, and environmental factors. Such systems require advanced design methodologies capable of addressing multiple, often conflicting objectives. In response to these challenges, this research introduces a comprehensive framework for multi-objective modeling in the conceptual design of complex systems, with a focused application in the shipbuilding industry. The primary objective is to establish a systematic approach for achieving Design for Excellence (DfX) through the simultaneous optimization of diverse performance criteria. The proposed mathematical model is rooted in the DfX principles and incorporates key indicators such as cost, performance efficiency, system reliability, and technology and manufacturing maturity. A distinctive contribution of this study is the integration of DfX principles with the multi-objective evolutionary optimization algorithm NSGA‑III, enabling the generation of Pareto-optimal solutions that balance conflicting objectives while accounting for incompatibility and correlation constraints among available technologies. The model is also highly flexible, allowing it to be adapted to various types of vessels with different operational requirements. A case study in the shipbuilding industry demonstrates the practical effectiveness of the proposed framework. The obtained solutions exhibit a high degree of balance among the competing objectives. Quantitative comparisons show that NSGA-III outperforms MOEA/D and the Epsilon-Constraint method in three of the four objectives (OMOE, reliability, and risk). Relative to MOEA/D and the Epsilon-Constraint method, NSGA-III achieves improvements of 7.75% and 35.20% in Hypervolume, 17.35% and 40.88% in Spacing, 22.54% and 411.76% in the number of Pareto solutions, and 31.25% and 72.73% in Inverted Generational Distance (IGD), respectively. Although NSGA-III requires approximately 31.47% more computational time than MOEA/D, it is 44.12% faster than the Epsilon-Constraint approach.These results confirm the efficacy of the proposed model and establish it as a practical decision-support tool for engineering design of complex systems.