Deep brain stimulation (DBS) is a neuromodulation therapy effective at treating motor symptoms of patients with Parkinson’s disease (PD). Currently, an open-loop approach is used to set stimulus parameters, where stimulation settings are programmed by a clinician using a time intensive trial-and-error process. There is a need for a systematic approach to tuning stimulation parameters based on a patient’s physiology. An effective biomarker in the recorded neural signal is needed for this approach. It is hypothesized that DBS may work by disrupting enhanced oscillatory activity seen in PD. In this thesis I propose and provide evidence for using a simple measure, called a phase response curve, to systematically tune stimulation parameters and develop novel approaches to stimulation to suppress pathological oscillations. In this work I show that PRCs can be used to optimize stimulus frequency, waveform, and stimulus phase to disrupt a pathological oscillation in a computational model of Parkinson’s disease and/or to disrupt entrainment of single neurons in vitro. This approach has the potential to improve efficacy and reduce post-operative programming time.