In time-triggered ethernet (TTEthernet), designing and optimizing the static scheduling of TT messages to improve the real-time performance of the whole network control system is important. When there is a high load on TTEthernet network communication, the conventional static scheduling policy introduces certain problems, such as increased packet loss rate, load imbalance, and transmission delay. Meanwhile, basic artificial intelligence algorithms generally features convergence within only a few steps, leading to higher probability to fall into a local optimal solution. To realize these targets, Fuzzy particle swarm optimization (FPSO) based on population diversity is established. By combining Particle swarm optimization (PSO) with a fuzzy algorithm, setting the adjustment of the inertia weight and the regulation of the mutation factor as the controlled variables of the Fuzzy logic controller (FLC), and adjusting the FLC inference rules, a novel static schedule generation for TTEthernet is proposed. Simulation results prove that, compared to the conventional rate-monotonic scheduling algorithm and the PSO algorithm, FPSO has a strong global search capability when the communication of the TTEthernet system is in a high-load state. FPSO improves the load balancing of the network and reduces the transmission delay of TT message packets. FPSO shows excellent ability to optimize scheduling tables and guarantee the real-time performance of the TTEthernet system.