Cloud computing provides a robust platform for deploying complex scientific applications, offering vast computational resources with flexible provisioning. However, achieving near optimal balance among multiple Quality of Service (QoS) parameters, such as cost, execution time, and load balancing, remains an ongoing challenge in workflow scheduling. While numerous scheduling approaches have been proposed over the years, many struggle to efficiently handle dynamic cloud environments and prevent resource overload. This paper presents the Hybrid Grey Wolf Optimization (HGWO) algorithm, an advanced scheduling approach that combines Grey Wolf Optimization (GWO) with Simulated Annealing (SA) to enhance workflow scheduling in cloud environments. HGWO optimizes makespan and execution cost while ensuring effective load balancing across virtual machines, thereby preventing resource congestion and improving efficiency. Additionally, a penalty function is introduced to eliminate solutions that violate budget and deadline constraints, ensuring strict adherence to QoS requirements. Experimental results demonstrate that HGWO outperforms standard GWO, particularly in maintaining balanced workloads and optimizing scheduling efficiency under dynamic conditions. By addressing multiple QoS factors while ensuring adaptability, the proposed approach provides a scalable and effective solution for cloud workflow scheduling. This work contributes to improving resource utilization and system reliability, offering promising directions for future research and practical applications.