Event-related paradigms have been used increasingly in the past few years for the localization of function in tasks involving overt speech. These designs exploit the differences in the temporal characteristics between the rapid motion-induced and the slower hemodynamic signal changes. The optimization of these designs and the best way to analyze the acquired data has not yet been fully explored. The purpose of this study is to investigate various design and analysis strategies for maximizing the detection of function while minimizing task-induced motion artifacts. Both event-related and blocked paradigms can be specifically designed to meet these goals. Various event-related and blocked designs were compared both in simulation and in experiments involving overt word reading in their ability to detect function and to avoid speech-induced motion artifact. A blocked design with task and control durations of 10 s and an event-related design with a minimum stimulus duration (SD) of 5 s and an average interstimulus interval (ISI) of 10 s were found to optimally detect blood oxygenation level-dependent signal changes without significant motion artifact. Ignoring images acquired during the speech can help recover function in areas particularly affected by motion but substantially reduces the detection power in other regions. Using the stimulus timing as an additional regressor to model the motion offers little benefit in practice due to the variability of the motion-induced signal change.