To meet the standards of Net Zero Energy Buildings, many researchers focus on coordinating the indoor thermal comfort, indoor air quality, and energy consumption of air-conditioning (AC) systems. The objective of this study is to take a university library as the research category to search for the optimized AC operating point among the air supply speed, temperature, and relative humidity (RH). The computational fluid dynamics (CFD) software, ANSYS FLUENT, combined with the DOE (Design of Experiments) method was used to optimize the AC operating point in the university library. Considering the range of the three AC operating conditions to be 1-2 m/s, 23-27°C, and 40%-70%, respectively, 15 simulating cases were generated by the DOE method. The effects of the three AC operating conditions on the PMV (predicted mean vote), indoor CO2 concentration, and energy consumption by the air-conditioner, were investigated in the 15 cases. After the 15 cases were completed, the Kriging method was adopted to build up the response surface (RS) among the three AC operating conditions and PMV, CO2, and energy consumption. Per the indoor thermal comfort satisfying −0.5<PMV<0.5 and indoor air quality under the condition of CO2<1000ppm, a multi-object genetic algorithm (MOGA) was used to find the optimized AC operating point with the lowest AC energy consumption on the RS. Finally, the optimized AC operating point was found to be an air supply speed of 1.0 m/s, air temperature of 23°C, and RH of 40%.