In well-mapped environments such as estate buildings, Unmanned Ground Vehicles (UGVs) play a crucial role in applications such as mail delivery, waste collection, and security surveillance. These vehicles operate autonomously or under remote human control. For autonomous UGVs, trajectory tracking controllers are essential for the wheels to accurately follow the desired path with minimal or no tracking error. The Proportional–Integral–Derivative (PID)-based control approach is one of the most widely adopted techniques for path tracking; however, its performance can degrade due to improper parameter tuning and external disturbances. Thus, this research aims to develop an optimized PID controller for trajectory tracking of a UGV in a virtual simulation environment, UGV3DSim. This was achieved by modeling and simulating a four-wheel UGV with front wheel steering control and rear wheel speed control in MATLAB/Simulink. The physical model of the UGV, comprising the chassis, top cover, and wheels, was designed using 3D modeling software and imported into the virtual environment. Three optimized PID controllers were developed using the Single Candidate Optimizer (SCO) algorithm, the Ali Baba and the Forty Thieves (AFT) algorithm, and the Walrus Optimizer (WO). These controllers were evaluated across various trajectory scenarios: linear and circular paths, go-to-goal navigation, and infinity-shaped trajectory tracking. The performance of the developed controllers shows that the WO-based controllers generated better trajectory tracking, had minimum overshoot, and settled faster in all three scenarios.