In order to maximize the effectiveness and performance of cloud computing systems, this study focuses on addressing the challenges of workload balancing and resource utilization in cloud scheduling. Workload balancing plays a crucial role in ensuring that computing workloads are evenly distributed across available resources, thereby reducing the likelihood of resource constraints and enhancing system performance. On the other hand, resource utilization aims to utilize processing power, memory, and network bandwidth to their fullest capacity, resulting in improved efficacy and cost-effectiveness of the cloud infrastructure. To tackle these challenges, we propose a novel optimization technique called CHPSO (Chi-squared Particle Swarm Optimization) in this context. The proposed algorithm demonstrates its effectiveness in optimizing resource utilization compared to other algorithms such as PSO (Particle Swarm Optimization) and CS (Cuckoo Search).