Buildings account for 40% of US energy usage. A building’s energy usage is largely determined by decisions made during its design. Such decisions relate to the form and orientation of the building, the materials used, and location of windows. A bi-objective building design optimization method was developed to minimize heating and cooling energy usage and maximize natural lighting (daylighting). Four optimization algorithms were integrated and evaluated based on efficiency and consistency of results. Thermal performance was evaluated by enclosure elements’ (windows, floors, walls, and roofs) impact on heating and cooling energy. Lighting performance was evaluated by the frequency and magnitude at which natural light levels deviated from a desired range. To ensure the accuracy of results, a method of thermal model calibration was developed based on room temperature responses to various weather conditions. The model used for simulation-based optimization was first calibrated to measured values, of the building it represented, such that predicted and measured hourly room temperatures (°F) deviated by an RMSE of 0.82 on hot and cold days. Optimization results show that thermal and lighting performance can be significantly improved from an initial design and the associated Pareto front aids evaluation of trade-offs between the two.