It is extremely difficult to conduct fundamental research on optimizing the system configuration and design parameters of energy conversion systems because many parameters need to be considered. This study ultimately aims to develop a methodology for realizing an energy system that utilizes available resources to generate a maximum product that employs the minimum number of components. Several studies have been conducted to find an optimal system configuration by decomposing energy systems into primitive process elements and sequentially searching for the optimal combination that uses the minimum number of constituent elements. This paper proposes a bottom-up methodology for defining and exploring configurations that combine elementary processes of energy systems with absorption technology. Absorption technology is a widely applied heat driven technology that is important for improving the energy efficiency of systems and also utilizes alternative energy resources. A specific procedure using a codification method is presented that generates new candidate configurations for the absorption system with respect to the optimization problem and enables an optimization algorithm to be used to implement the organized rules. When applying the proposed methodology to optimization, designers should narrow promising solutions by conducting the optimization under simplified and/or idealized conditions, and then adjusting the solutions by considering certain real conditions. One example of applying this optimization is shown to reveal the capability of the proposed methodology in clarifying a basic configuration that generates the maximum product under constant heat source capacity conditions. The demonstration shows that the existing absorption system, which is calculated based on the experience of designers, could be derived by automatically performing the optimization using this methodology. The proposed methodology is significant for use in realizing an optimized absorption system, and it allows designers to predict all possible configurations in advance and clarify a simple and feasible optimal system configuration.