The article proposes a stochastic optimized-submovement model to explain Fitts' law, which describes the logarithmic trade-off between the duration and spatial precision of rapid aimed movements. The model suggests that an aimed movement involves primary and optional secondary corrective submovements, programmed to minimize average total movement time while maintaining high accuracy. The submovements are adjusted to optimize the average magnitudes and durations of noisy neuromotor force pulses. The model explains various empirical findings from human motor performance literature and is supported by two new experiments on rapid wrist rotations. These experiments show that primary submovement durations and spatial endpoints conform to a square-root approximation of Fitts' law, and that submovement optimization is robust under conditions without visual feedback. The model provides insights into the principles of motor performance and links it to research on sensation, perception, and cognition.The article proposes a stochastic optimized-submovement model to explain Fitts' law, which describes the logarithmic trade-off between the duration and spatial precision of rapid aimed movements. The model suggests that an aimed movement involves primary and optional secondary corrective submovements, programmed to minimize average total movement time while maintaining high accuracy. The submovements are adjusted to optimize the average magnitudes and durations of noisy neuromotor force pulses. The model explains various empirical findings from human motor performance literature and is supported by two new experiments on rapid wrist rotations. These experiments show that primary submovement durations and spatial endpoints conform to a square-root approximation of Fitts' law, and that submovement optimization is robust under conditions without visual feedback. The model provides insights into the principles of motor performance and links it to research on sensation, perception, and cognition.