The global energy demand increases, giving rise to concerns about fuel scarcity and environmental damage. In addition, world energy consumption rises as a result of production. In the industrial sector, efficient production scheduling can help reduce energy use. This article proposed a Hybrid Aquila Optimizer (HAO) for solving the Hybrid Flow Shop Scheduling Problem (HFSSP) to minimize total energy consumption. HAO was implemented to determine the best sequence job with the best total energy consumption. Ten job variations were presented to be analyzed on TEC and computational time to measure algorithm performance. This study compared the proposed HAO algorithm with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Tiki-taka, Firefly, and Artificial Bee Colony (ABC) algorithms. The result of the experiment indicated that HAO was more efficient in reducing the total energy consumption than the GA, PSO, ACO, Tiki-taka, Firefly, and ABC algorithms. A comparison of computational times for each algorithm is also presented in this study.