Traditional methods for item selection in computerized adaptive testing only focus on item information without taking into consideration the time required to answer an item. As a result, some examinees may receive a set of items that take a very long time to finish, and information is not accrued as efficiently as possible. The authors propose two item-selection criteria that utilize information from a lognormal model for response times. The first modifies the maximum information criterion to maximize information per time unit. The second is an inverse time-weighted version of a-stratification that takes advantage of the response time model, but achieves more balanced item exposure than the information-based techniques. Simulations are conducted to compare these procedures against their counterparts that ignore response times, and efficiency of estimation, time-required, and item exposure rates are assessed. (Contains 2 tables and 1 figure.)