With the widespread use of computers in modern assessment, online calibration has become increasingly popular as a way of replenishing an item pool. The present study discusses online calibration strategies for a joint model of responses and response times. The study proposes likelihood inference methods for item paramter estimation and evaluates their performance along with optimal sampling procedures. An extensive simulation study indicates that the proposed online calibration strategies perform well with relatively small samples (e.g., 500~800 examinees). The analysis of estimated parameters suggests that response time information can be used to improve the recovery of the response model parameters. Among a number of sampling methods investigated, "A"-optimal sampling was found most advantageous when the item parameters were weakly correlated. When the parameters were strongly correlated, "D"-optimal sampling tended to achieve the most accurate parameter recovery. The study provides guidelines for deciding sampling design under a specific goal of online calibration given the characteristics of field-testing items.