This work presents a framework to reduce energy consumption in Selective Laser Sintering (SLS) through part decomposition which involves decomposing complex models into smaller sub-assemblies, and concurrently optimizing their build orientation. A Genetic Algorithm (GA) based approach is utilized to determine the optimal cutting planes for part decomposition and part orientations, ensuring a reduction in energy consumption is achieved. The methodology section details the framework and the optimization technique employed, and the effectiveness of this framework in reducing energy consumption and enhancing economic productivity in SLS is demonstrated. This framework was tested on three components: an angled bracket, an axial fan blade, and the Stanford Bunny, and the results showed a reduction of 21.9, 20.3, and 21.0% in energy consumption, respectively. The adaptability of this framework to different geometries highlights its potential for enhancing efficiency of SLS or other AM processes. This research makes a significant contribution by offering a comprehensive solution to improve the energy efficiency of SLS technology and provides designers with a simulation-based tool to achieve sustainable manufacturing practices. In future, the framework will be extended to other AM processes and more test cases will be evaluated to increase reliability and applicability.