Improving building energy performance is central to promote sustainability and mitigate climate change, but it has proved to be a challenging process requiring investigation of a large domain of energy retrofit measures and contrasting objectives. Besides, the unique characteristic of Iran, namely heavily subsidized energy prices and tiered utility rates, further complicate the problem. This research introduces a novel simulation-based multi-objective optimization approach based on a modification of NSGA-III algorithm, denoted as prNSGA-III, powered by parallel computing design and result-saving archive that offers an increased computational potential. The method is developed by integrating EnergyPlus as energy simulation engine with optimization algorithm coded in MATLAB programming language. A large domain of active, passive, water conservation, and renewable retrofit measures are explored to maximize environmental performance while minimizing thermal discomfort and life cycle cost. The method is applied to a residential building in Tehran, Iran, subjected to different scenarios with reference to future climate change and energy price variation. The results show that implementing suggested energy retrofit measures can yield substantial improvement in environmental performance and curb CO-eq emission by up to 73%, and also reduce thermal discomfort by up to 46%. However, energy-efficient solutions can only be achieved at the expense of increased life cycle cost. Moreover, the result demonstrates that climate change can affect optimal solutions, particularly heating and cooling systems, due to changes in external thermal loads.