Optimization problems in the research literature are typically simplified and/or heavily constrained and focus on a single set of physical processes. Real-world nuclear engineering problems feature competing multi-physics phenomena and require equally complex analysis. To prove its usefulness in this area, optimization must demonstrate an ability to handle many competing objectives whilst accurately simulating the reactor environment. This paper applies the MOJADE optimization algorithm to two design problems, a 3D PWR Supercell and a 3D BWR fuel assembly, evaluating performance objectives related to neutronics and thermal hydraulics simultaneously, using the concept of Pareto dominance. In both cases, MOJADE was able to find competitive or non-dominated designs compared to baseline solutions generated from the literature and required no control parameter tuning or training time. Analysis revealed that MOJADE can identify key variables which impact objective performance, demonstrating the algorithm’s ability to provide new insight to complex 3D problems featuring multi-physics analysis.