Modern industrial production planning and control (PPC) systems are responsible for supporting planning decisions on how to optimally produce a given set of products while minimizing costs and retaining production constraints, such as delivery tardiness or offtimes. In recent years, more and more attention has also been paid on energy efficiency as part of production optimization, resulting in competing optimization targets. In order to solve such complex multi-objective scheduling problems in practice, metaheuristic methods are used because of their ability to deliver acceptable solutions in feasible time. In this paper, we demonstrate the application of a General Variable Neighborhood Search (GVNS) metaheuristic on a case study of flow shop scheduling in an industrial bakery in different scenarios and study the effect of different energy prices on the planning result. The case study features a simple production line with thermal processes for baking and freezing and also incorporates the energy supply system as well as a model of the thermal building hull. The metaheuristic is combined with a hybrid discrete/contin-uous simulation model to evaluate the energy efficiency of different production scenarios. The hybrid simulation enables to accurately capture material and energy flow within the production in an integrated and dynamic manner. Overall, this simulation-based optimization method is intended to support energy-aware production scheduling in practical applications.