Traffic signal control is one of the most cost-effective means of improving urban mobility. Signal control can be categorized as pretimed, actuated, and adaptive. Among these, both pretimed and coordinated actuated controllers deploy multiple signal timing plans to account for traffic demand changes during the day, whereas adaptive control changes the timing plan in real time according to traffic conditions. In the case of pretimed and coordinated actuated signals, morning peak traffic would differ from that of the off-peak such that it would be better to use two distinctive signal timing plans. Traffic engineers often determine such time-of-day (TOD) breakpoints manually by using 1 or 2 days' worth of traffic data. A few recent studies developed statistical and heuristic methods for TOD breakpoints by using archived traffic data. These approaches determined the breakpoints through minimization of within-cluster distance and maximization of between-cluster distances. Thus, the clusters do not directly reflect the performance of timing plans and often result in only local optimal TOD breakpoints. One method is based on a genetic algorithm (GA) that optimizes TOD breakpoints with explicit consideration of signal timing performance at a representative intersection. The proposed method implements two-stage optimizations: outer loop for TOD breakpoints and inner loop for timing plans of corresponding intervals. The proposed approach is implemented on a network consisting of three coordinated actuated signalized intersections. The convergence graphs of both inner-and outer-loop optimization indicate that the GA-based algorithm obtains breakpoints within a relatively small number of iterations. Also studied was the performance of the proposed approach for a varying number of breakpoints (i.e., four to eight). The results, based on a microscopic simulation program, Sim Traffic, indicated that six breakpoints outperformed the other numbers of breakpoints considered.