With the increasing operational complexity of power grid business and the rising demand for intelligence and automation, digital transformation has become a crucial direction for power grid development. To enhance power grid management efficiency and reduce operational costs, it is particularly important to conduct research on power grid optimization scheduling using modern digital information technologies. This study proposes a hybrid optimization scheduling method that combines genetic algorithms (GA) and particle swarm optimization (PSO), aiming to minimize power grid operational costs while ensuring system stability. Experimental results demonstrate that the GA-PSO algorithm consistently exhibits lower scheduling costs throughout the optimization process, especially after 1000 iterations, where the scheduling cost drops to 3200, significantly lower than other algorithms. GA-PSO not only optimizes power grid scheduling costs but also performs exceptionally well in constraint satisfaction, with a constraint violation of 50 kW, far below other methods. Overall, the research findings confirm the superiority of the GA-PSO algorithm in power grid scheduling, particularly in complex environments with load fluctuations, energy instability, and grid faults, demonstrating higher adaptability and robustness. This provides strong technical support for the digital transformation of power grid business.