The building design process plays a central role in efforts to implement energy-efficient practices. However, unilateral design choices based solely on reducing operational energy use can significantly increase a building’s embodied energy and life cycle energy use as there is a trade-off between embodied and operational energy. To support such trade-off problems, multi-objective optimization represents a useful approach that produces a set of optimal solutions from where a solution can then be selected and progressed within the design process. Selecting one solution from the set of optimal solutions can however be a challenging task as each solution has the potential to be chosen as the optimum. Therefore, the purpose of this study was to explore how solutions from a multi-objective optimization approach can be analyzed further to provide information to decision-makers when selecting the optimal design solution. An approach is proposed where the integration of post-optimization sensitivity analysis into a multi-objective optimization approach aims to support decision-makers in analyzing the optimal solutions provided by the optimization process. The applicability of approach is demonstrated using a case of a multifamily apartment building located in Sweden, where the aforementioned trade-off is explored for a set of energy efficiency measures. Thereby, a diverse range of optimal solutions that could result in up to 4520 GJ life cycle energy (LCE) savings relative to the case building’s initial design was initially identified using the multi-objective optimization. These solutions were then subjected to a sensitivity analysis where the results indicated that in general the lowest and highest sensitivity in terms of LCE use belonged to the insulation thicknesses in roof and walls, respectively. Furthermore, the thickness of exterior floor insulation yielded the greatest variation in the sensitivity. The findings of case study indicate that the post-optimization sensitivity analysis can add valuable information that complements the results obtained using a multi-objective optimization approach. Consequently, it can support decision-making on how to progress with the design in terms of what design parameters have a negligible or significant impact on the objectives when they are varied, thus facilitating prioritization.