Heating, ventilation, and air conditioning (HVAC) systems are notorious for their high energy consumption in buildings, particularly in regions with extreme cooling or heating demands. Air filters play a vital role in these systems, affecting both energy efficiency and indoor air quality. However, high-efficiency filters, due to their significant increase in airflow resistance, require excessive energy compared to low-efficiency filters. This poses a challenge in finding the optimal compromise between reducing energy consumption and enhancing indoor air quality. To address this challenge, a meticulous selection process is crucial in achieving a middle ground that satisfies both objectives. Proper sizing and design of air filters are therefore essential for successful HVAC projects. This paper introduces the utilization of optimization techniques as decision-support tools to determine the optimal design parameters of commonly used HVAC air filters under various scenarios. The developed model incorporates multiple objectives and design criteria, including life-cycle cost (LCC), filter size, and efficiency. By leveraging the differential evolution optimization technique, an algorithm is developed to forecast a range of optimal solutions (Pareto front) based on predefined system criteria and boundary conditions. The model is extensively tested and demonstrates exceptional performance in returning optimal solutions, in addition to the capability of narrowing down and converging to a single value. This methodology holds significant potential in assisting investment decisions concerning HVAC air filters, providing valuable insights for optimizing energy efficiency while ensuring satisfactory indoor air quality.