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This study considered the trade-off between energy consumption and thermal comfort as a multi-objective optimization problem and proposed a novel and practical solution by utilizing empirical energy models of the ACB system and an evolutional non-dominated sorting genetic algorithm II. Chilled water flow rate, primary airflow rate, and room temperature in ACB systems are specifically chosen as control variables due to the control convenience. Besides, a parameterless selection strategy that considers both thermal comfort and energy consumption is proposed to select the most appropriate solution among Pareto optimal solutions. Three steady-state experiments with different heat load conditions are conducted. Compared to experienced operation, the proposed strategy demonstrates a maximum of 39.32% of energy saving and 12.21% of PPD reduction by increasing the water flow rate and room temperature, and reducing the primary airflow rate.