In response to the mounting challenges of climate change and the ongoing increase in carbon emissions, energy rationalization has emerged as a key concern in recent years. The industrial sector, being one of the major energy consumers, has therefore received significant attention from researchers seeking effective solutions to improve energy efficiency. Among the promising approaches, production scheduling has emerged as a cost-effective means to reduce energy usage without requiring major infrastructure investments. This paper proposes an energy-efficient scheduling approach aimed at minimizing the total energy consumption in a manufacturing environment. Specifically, we address the Flow Shop Scheduling Problem (FSSP) with job groups, where production sequences are optimized to balance operational efficiency and energy performance. To this end, two metaheuristic algorithms are developed: An Iterated Greedy (IG) algorithm and a Genetic Algorithm (GA), both adapted to handle energy-aware scheduling constraints. A comprehensive numerical study is carried out to assess the performance of the proposed methods, demonstrating their effectiveness in achieving significant reductions in total energy consumption while maintaining high scheduling quality.