In this paper, we tackle the crucial challenge of optimizing energy efficiency in the industrial sector, focusing on the Distributed Blocking Flow Shop Problem (DBFSP) under Position-Dependent Processing Time (PDPT) constraints. Our primary focus is the minimization of total energy consumption (TEC) in distributed manufacturing environments. To address this, we propose two metaheuristic approaches: an Enhanced Genetic Algorithm (EGA) and an Enhanced Migratory Birds Optimization (EMBO). These approaches are designed to efficiently solve the problem while considering its complex constraints. To validate their efficacy, we conduct extensive computational experiments on benchmark instances, comparing our EGA and EMBO against a baseline algorithm. Results demonstrate the superiority of the proposed methods, with EGA achieving the lowest TEC in 75% of cases. This work provides actionable insights and a practical foundation for further advancements in energy-efficient scheduling and sustainable manufacturing practices.