Abstract: Aimed at the problems of low utilization rate and unbalanced load of current high performance computing task scheduling strategies, a high-performance computational task scheduling algorithm based on genetic ant colony optimization (GA-ACO) is designed. GA-ACO was divided into two stages. In the first stage, the genetic algorithm was used to narrow the space and quickly find the excellent solution, and then it was transformed into the initial pheromone of ant colony algorithm. In the second stage, a global update strategy based on ant colony pheromone was proposed to optimize the convergence speed. Experimental analysis shows that compared with ant colony algorithm and genetic algorithm, this algorithm shortens the task completion time and reduces the node load rate.