Integrated solar absorption cooling and heating (SACH) systems, which use solar energy to provide space heating, space cooling, and water heating, represent a promising substitute to reduce the earth's carbon emissions. SACH systems currently are designed based on engineering experience for the most part and few systematic methodologies are available to identify the key optimal parameters for SACH systems, such as the slope of the solar collectors, the area of the solar collectors, and the volume of the storage tanks. As a result, the established systems usually are not capable of yielding the greatest returns on investment. Motivated by the above facts, this study investigates a formal method for SACH system optimization by incorporating simultaneously a system's performance related to its economic, energy, and environmental aspects. The proposed method includes central composite design, regression, and multi-objective optimization. Central composite design (CCD) is used to select the significant experimental data generated by energy system simulation and life cycle analysis. Linear regression models are used to predict the functional relationship between system performance and the key system parameters using data sets. A multi-objective optimization model is then formulated and solved based on the Weighted-Tchebycheff metric approach. The proposed approach is applied to medium-sized office buildings located in Phoenix, Los Angeles, Atlanta, and Chicago; and the results suggest that the approach can provide a systematic mechanism to optimally design SACH systems. Published by Elsevier Ltd.