An important share of the energy demand of buildings is attributed to the heating, ventilation, and air conditioning (HVAC) systems. Simple changes in the operational settings of these systems, such as adjusting the thermostat setpoint temperatures, can have a significant impact on building performance (e.g., energy consumption and costs). In parallel, changes in indoor environmental conditions can directly impact occupants’ comfort, wellbeing, and productivity. A review of the literature indicates that the stated metrics of building performance are often studied in isolation, failing to capture their cross-effects and potential implications for building operation strategies. This chapter presents a genetic algorithm (GA) multi-objective optimization (MOO) that captures the trade-offs between—and optimizes—three competing objectives of building performance: (i) energy consumption, (ii) thermal comfort, and (iii) productivity. Using building performance simulation (BPS), models of three archetype office buildings located in different climate zones are used to showcase and validate the framework’s capabilities. Optimal HVAC setpoint settings are found to reduce energy consumption by up to 25.8% while maintaining acceptable comfort and productivity levels of occupants. Additionally, the non-dominated solutions for buildings located in different weather zone vary statistically, motivating the need for climate-sensitive HVAC operation strategies and standards.