Energy is expensive and a potential way to reduce energy consumption may be through using intelligent scheduling techniques. In this paper, we propose a genetic algorithm to solve a single machine scheduling problem where the objectives are minimizing total completion time and energy consumption of manufacturing equipment when all of the processing time and release dates of the job are known. This problem has several applications including scheduling in manufacturing industry, energy minimization in computers, cell phones, sensors, etc. Different fitness functions and parameters depending on the problem's characteristics were tested using a design of experiment approach. The proposed methodology generates several pareto optimal solutions. A decision maker can select one of these solutions using Analytical Hierarchical Process.