Particle Swarm Optimization (PSO) has been widely applied to solve continuous optimization problems due to its simplicity and fast convergence. However, conventional implementations of PSO often assume synchronous updates and fixed iteration steps, which limit the algorithm’s ability to accurately reflect dynamic interactions among particles, especially under time-varying conditions and computational constraints. This paper proposes a discrete event simulation (DES)–based approach for implementing PSO, in which particle movements and velocity updates are modeled as asynchronous events in continuous time. The proposed framework allows particles to interact and update their states independently, closely resembling natural swarm behavior. As a result, the DES-based PSO improves convergence speed, reduces the risk of premature convergence, and enhances overall optimization performance. Simulation results clearly demonstrate the effectiveness and advantages of the proposed approach compared with traditional time-stepped PSO implementations.