This dissertation develops a multiobjective framework for energy-aware operations planning in order to optimize the energy cost and scheduling objectives. Using frameworks developed for different operational settings, the decision-maker has the opportunity to implement a schedule with an energy cost and other scheduling performance measures in the presence of dynamic electricity prices. In this dissertation, mixed-integer mathematical models are developed and solved for multiobjective problems (on single or parallel machines) with minimization of both scheduling criteria and energy cost. The problems considered in this dissertation are as follows: non-preemptive single machine to minimize total tardiness and total energy cost; preemptive single machine to minimize total completion time and total energy cost; preemptive single machine with non-preemptive sequence-independent setup time to minimize total completion time and total energy cost; and non-preemptive parallel machine with load balancing, total completion time, and total energy cost. The proposed models were solved using exact solution methods such as weighted sum and e-constraint, as well as metaheuristic approaches such as genetic algorithms, the ant colony optimization algorithm, and the greedy randomized adaptive search to obtain good approximate sets of non-dominated solutions in a timely fashion. Based on his/her preference, the decision-maker can use a selection method such as the technique for order preference by similarity to ideal solution (TOPSIS) or multi-objective optimization on the basis of ratio analysis (MOORA) to implement a schedule among all non-dominated solutions that minimizes some other secondary objectives.