Autonomous shovel digging of loaders is the key technology to realise automation and intelligent operation. The effective tracking control for the target operation trajectory is one of its core parts. Proportional–integral–derivative (PID) and other control methods without system models have issues, such as large overshoot amplitudes and jitter phenomena under system constraints. Given that model predictive control (MPC) effectively deals with system constraints to ensure smooth operation, this paper introduces MPC into motion control for the loader’s working mechanism and proposes a trajectory tracking control method based on nonlinear model predictive control (NMPC). This study shows that, under the same system constraints for different target operation trajectories, the designed controller achieves better tracking performance than conventional PID and sliding-mode control (SMC) controllers in handling system constraints and ensuring smoothness. It is also found that the tracking performance decreases as the dig insertion depth increases. Therefore, trajectories with larger dig insertion depths are not recommended as viable operation trajectories. This study provides an important foundation and new insights for improving the control performance of the loader’s working mechanism.