In the field of computational workflow management, optimizing execution time, resource utilization, and cost has become increasingly crucial. This research proposes a new way for efficient workflow scheduling based on the Multi-Objective Particle Swarm Optimization algorithm. First, we divide the workflows into distinct tasks, which we then map to virtual machines. The proposed research offers a new hybrid strategy that uses MOPSO and GD to optimize workflow scheduling in cloud computing. Cost, latency, and energy usage are critical features of performance, but optimizing them will most certainly be a trade-off. The details of the optimizing workflow mapping methodology are described, together with the method’s performance evaluation results in terms of cost, time, and energy consumption. The presented hybrid method consistently produced results with lower cost, time span, and energy utilization. The study makes major contributions to the existing state of research by employing a robust, adaptive, and optimized method that can considerably improve efficiency and usage of resources in cloud environments. The fact that the developed method surpassed earlier studies has implications for the future, since it can be used as a benchmark for subsequent studies in multi-objective optimization.