To solve the problem of low scheduling efficiency and resource utilization rate of large-scale multithreaded testing tasks in automatic test of avionic equipment, a load balancing screening mechanism is designed, a parallel testing static resource scheduling model is established, and an improved Particle Swarm Optimization (PSO) task scheduling algorithm based on particle encoding and decoding is proposed. Bidirectional learning or gravitational/repulsive force mechanism is selected through chaotic initialization sequence and decision weight selection, thus the efficiency and accuracy of PSO is improved. On this basis, in response to the rescheduling problem in automatic testing, different objective functions are established based on the urgency of the test piece to complete the test of urgent parts during the testing process, and thus the dynamic planning ability of the scheduling method is improved. Simulation experiments have verified that the scheduling method can effectively improve the scheduling efficiency and resource utilization rate of parallel testing tasks.