Effective application scheduling in cloud environments is crucial for optimizing operational efficiency and resource utilization. This paper addresses the inherent complexities of this problem by introducing an innovative approach, focused on reducing operational costs, particularly in terms of energy consumption and application migration expenses. The methodology involves a two-stage decision-making process, supplemented by a suite of integrated and compatible heuristics, which together ensure improved efficiency and manageable runtime. Validated through comprehensive experiments against a public dataset and industry-standard contests, our approach demonstrates significant advancements in cloud scheduling, offering scalable and effective resource management solutions for cloud environments.