Resource allocation is still an open issue within mist computing, particularly for energizing IoT and digital twin (DT) applications. As the engagement of DT increases in IoT, the judicious scheduling for energy efficiency will be crucial for longevity of the device. In this paper, we propose a hybrid GA-PSO based heuristic model for resource scheduling in mist computing to achieve optimum energy utilization and support DT application. Compared to GA’s genetic exploration and PSO’s convergence, hybrid GA-PSO enhances and adapts crossover and mutation levels to IoT workload scheduling. Based on the obtained results, decreased energy consumption, increased speed of computations, and optimization of resources all point to the effectiveness of hybrid algorithms in sustainable IoT and DT applications in mist environments.