Abstract: The ground stations of modern aerospace telemetry, tracking, and command (TT&C) systems face the dual challenges of diversification of measurement and control task types and a sharp increase in resource demands. Traditional ground station baseband processing resource pools are limited in resource utilization and system performance due to their relatively solidified scheduling and lack of flexibility. To address the above problems, this paper proposes a dynamic resource scheduling strategy based on a population perturbation genetic algorithm. The algorithm introduces a global combinatorial encoding method, elitist individual preservation strategy, population perturbation mechanism, non-traditional multi-stage random partial crossover, and parallel single-point and two-point mutation operations, thereby enhancing scheduling flexibility and responsiveness. Through a series of simulation experiments, the results demonstrate that compared to traditional algorithms, the proposed population perturbation genetic algorithm can quickly achieve dynamic baseband processing resource allocation, resulting in significant improvements in task processing efficiency, it performs particularly well in handling high-load tasks, effectively enhancing the utilization and operational efficiency of ground station resources.