Power utilities currently manage unpredictable electrical demand through the use of fast-ramping plants and punitive demand fees. Unpredictable demand makes it more difficult to onboard variable renewable energy sources, such as solar and wind, because of their intermittency, which can contribute to grid instability. Optimal utilization of variable renewable energy sources requires a flexible, resilient electrical grid and thus a transformation of the current system to a proposed “smart grid” that would adapt in real time to grid signals. Batch manufacturers are well suited to reduce the burden of intermittency by considering demand side management techniques when scheduling process operations. The goal of such participation is a reduction in electrical demand and associated costs for both the facility and the utility. This study investigates how a novel application of the genetic algorithm could be used to schedule a batch manufacturer’s operations in a manner that reduces overall peak demand and is compatible with process constraints. The genetic algorithm is chosen because it is highly efficient at finding minima and handling complex constraints and large data sets. A major highlight of this article is the use of measured demand profiles to log real-time energy consumption of process equipment. The scenarios investigated use a genetic algorithm to determine the optimum scheduling when given a fixed set of interdependent operations that must be executed within the day. All investigated scenarios have a reduction in peak demand after rescheduling via the genetic algorithm. Baseline operations demonstrated reductions between 13 % and 28 %, with an average reduction of 21 %. The results of this study demonstrate that automated load shifting can easily be applied to an industrial facility and paves the way for future work to investigate the integration of scheduling algorithms with process control systems.