Research on Genetic Algorithm (GA)-based Workflow Scheduling in Cloud Computing focuses on applying evolutionary optimization techniques to efficiently allocate and schedule tasks within complex cloud workflows. This approach aims to minimize execution time, cost, and energy consumption while ensuring compliance with Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Key research directions include designing GA variants customized for dynamic and heterogeneous cloud environments, hybrid GA approaches combined with other metaheuristics (e.g., PSO or ant colony optimization) to improve convergence and solution quality, and priority-aware GA scheduling strategies for task-critical workflows. Other emerging topics involve multi-objective GA for simultaneously optimizing makespan, cost, and energy efficiency, adaptive GA for real-time workflow scheduling, and cloud–edge integrated workflow optimization. Additionally, research on handling uncertainties, fault tolerance, and resource failures in GA-based scheduling, as well as integrating machine learning to enhance GA performance and decision-making, represents significant avenues for advancing intelligent and efficient cloud workflow management.