Cloud computing has emerged as an essential component of green computing in recent years, allowing for effective sharing of storage and processing resources. However, resource scheduling is still one of the toughest jobs in this regard. In this paper, a new approach to resource scheduling has been proposed that will aim to reduce execution time by enhancing energy efficiency and optimizing performance. Our proposed greedy strategy algorithm makes locally optimum decisions at every step, allowing the algorithm to allocate resources efficiently. Further, we compared its performance with various traditional algorithms, including First Come, First Serve, Shortest Job First, Round Robin, and the Generalized Priority Algorithm concerning energy efficiency and CPU utilization. Our experiments, performed in a Python environment, demonstrated that the greedy strategy has the highest CPU utilization, at 92.50%, while the execution time and energy consumption decreased. In this way, the approach decreases the completion time for the submitted tasks while increasing user satisfaction with timely decision-making.