  Brownian motion with known positive drift is sampled in stages until it
crosses a positive boundary $a$. A family of multistage samplers that control
the expected overshoot over the boundary by varying the stage size at each
stage is shown to be optimal for large $a$, minimizing a linear combination of
overshoot and number of stages. Applications to hypothesis testing are
discussed.
