This dissertation develops operational methods for minimization of energy consumption of manufacturing equipments. Using the frameworks developed, a significant amount of energy can be saved when non-bottleneck and/or underutilized machines/equipment are turned on during a long idle time. In this dissertation, mathematical models are developed for multi-objective problems (on single or parallel machines) with minimization of a scheduling criteria and minimization) of total energy consumption. Metaheuristics approaches have been developed to solve the resulting mathematical models. Genetic algorithm, greedy randomized adaptive search procedure, and hybrid metaheuristic based approaches are utilized to obtain good approximate sets of non-dominated solutions in reasonable amount of time for different optimization problems. The decision-maker can use the results from these frameworks to plan for energy efficient production. The methods to obtain an approximate pareto front is combined with a selection method such as the Analytic Hierarchy Process to obtain a full schedule that minimizes other secondary objectives. Further research includes studying the reliability of the machine under repeated turned-off and turned-on. Also a maintenance model could be developed to include the energy minimization framework. This additional model would be useful in scheduling maintenance team with the objective of decreasing the costs associated with breakdowns including the energy-related cost. Another area of interest might be on developing a model to minimize energy consumption and scheduling objectives for machines with multi-state sleep modes.