With the rapid increase in energy prices and growing environmental concerns, enhancing energy efficiency has become a global strategic priority, driving manufacturing industries toward consumption optimization. However, unexpected breakdowns and operational deficiencies lead to increased energy consumption. The adoption of efficient maintenance systems can play a crucial role in reducing operational costs and job completion times. In this regard, the Condition-Based Maintenance (CBM) approach enables real-time monitoring of equipment status and identifies as well as implements appropriate preventive and corrective actions. Accordingly, this study presents an integrated model of a flexible production scheduling problem equipped with a CBM system, which addresses online maintenance scheduling, job sequencing, and resource allocation. In the production environment, energy consumption is a function of job completion time; therefore, in the optimization process, both makespan and power consumption are simultaneously considered with the objective of optimizing electricity usage at the factory level. Considering the NP-Hard nature and stochastic characteristics of the problem, two metaheuristic algorithms, namely the Genetic Algorithm (GA) and the Biogeography-Based Optimization (BBO), are employed to obtain near-optimal solutions.