Recent research in Genetic Algorithm-based workflow scheduling in cloud computing emphasizes optimizing task execution to achieve minimal makespan, reduced cost, and efficient resource utilization. Genetic Algorithms (GA) are widely applied due to their strong global search capability, adaptability, and robustness in handling complex workflow dependencies across heterogeneous virtual machines. Modern studies enhance the traditional GA framework by integrating hybrid strategies such as PEFT heuristics, adaptive mutation rates, and multi-objective optimization to balance time, cost, and energy efficiency. These approaches enable dynamic and scalable workflow scheduling, ensuring improved performance, load balancing, and Quality of Service (QoS) in large-scale cloud infrastructures.