Data centers have experienced dramatic growth in recent years in order to meet the ever-increasing demand for computing. As a result, minimizing the electrical cost to operate data centers has become a crucial issue. In this paper, we observe that electricity prices change over time, and that we can take advantage of periods with low prices by scaling up processor speeds to perform more work, while scaling down speeds during high price periods to reduce cost. We apply this observation to several settings. First, we consider an offline setting which assumes future electricity prices are given, and propose an efficient algorithm for optimally scaling a processor's speed in order to minimize the total electrical cost for completing a task by a deadline. We then consider a more realistic stochastic setting in which future prices are not known, but vary according to a Markov model. We present another efficient algorithm for minimizing the expected cost to meet a deadline. We performed a number of experiments using real electricity price traces to test the performance of our algorithms. We show that our stochastic algorithm is light-weight and relies only on easily obtainable price data, but that it achieves excellent performance, with only a 1 percent cost difference on average from the optimal offline algorithm. In addition, the stochastic algorithm significantly reduced costs compared to several candidate algorithms.