PARP inhibitors (PARPis) have revolutionized ovarian cancer treatment, yet these drugs often fail after a few months due to emerging drug resistance. A recent clinical trial in prostate cancer showed that evolutionary-inspired, adaptive drug scheduling significantly delayed time to progression. This approach adaptively skipped treatment to maintain a pool of drug-sensitive cells that suppressed resistant cells through competition. Here, we present results from a combined modeling and experimental study in which we investigated whether adaptive therapy could delay resistance to the PARPi Olaparib in ovarian cancer. We performed a series of in vitro experiments in which we used Incucyte Zoom time-lapse microscopy to characterize the cell population dynamics under different PARPi schedules. Leveraging these data we developed an ordinary differential equation mathematical model of treatment response, and used this model to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumor growth, even in the absence of any resistance. This is because multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Subsequent experiments confirm this prediction in vivo. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis, and showcases some of challenges involved in developing adaptive therapies for new treatment settings.