Considering server data is growing exponentially, cloud-based task scheduling and load balancing are essential fields of study. Task scheduling, load balancing are optimized via cloud resource and task monitoring. It is easily adaptable to the complexity and scale of the cloud environment. We propose utilizing a hybrid genetic algorithm that combines genetic algorithm and water wave optimization to coordinate and distribute tasks in the cloud. The best solution from the final population is then used to optimize task scheduling and load balancing approaches. It significantly reduces the inconsistent performance of cloud-based tasks. The experimental simulation result demonstrates that the proposed approach reduces the Make span by 7% to 16%, Transmission Time by 21% to 30%, Average Waiting Time by 5% to 18%, Degree of Imbalance by 62% to 74%, and enhances resource utilization by 2% to 30% on average, in comparison to the PSO, LOA, ACO, Firefly Optimization, SFO and ABC approaches for ten VMs.