This work addresses the challenge of reducing energy costs in manufacturing systems operating under time-of-use pricing while integrating renewable energy and storage capabilities. We study a flexible flow shop scheduling problem that incorporates on-site photovoltaic generation, an Energy Storage System, and participation in Demand Bidding programs. The problem is formulated as a bi-objective optimization model aiming to minimize makespan and total energy cost, accounting for renewable availability, storage constraints, and time of use tariffs. To solve this NP-hard problem, we develop a tailored NSGA-II algorithm based on an Order-Oriented Modeling Scheme, combining sequence-based encoding with stage-wise machine assignment. The method integrates problem-specific genetic operators and a fitness evaluation that captures production and energy interactions. Computational experiments on benchmark instances demonstrate the algorithm's ability to produce diverse and high-quality Pareto fronts, with performance close to optimal solutions on small instances and strong convergence indicators on larger ones. The results highlight the relevance of metaheuristic approaches for supporting energy-efficient production planning in environments combining renewable energy, storage, and electricity pricing.