Compared to the work of Klise et al. and Zi et al. , we use a much higher spatial resolution to define possible sensor locations resulting in finding a sensor placement with higher detection efficiency. Also, we utilize all available wind data for a site with a resolution of one minute to achieve a more precise approximation of the actual meteorological conditions at the site. Moreover, we simulate methane emissions with the Gaussian puff model rather than the Gaussian plume model owing to its superior accuracy in capturing the dynamics of atmospheric dispersion by allowing for variable wind conditions. As a result of the aforementioned enhancements, our sensor placement optimization algorithm delivers solutions of increased accuracy and granularity, thereby offering substantial benefits for practical applications. As such our algorithm offers an effective solution for optimal sensor placement which can serve as an open-source reference tool for CMS technology vendors and advance the quest towards rapid elimination of methane emissions on oil and gas sites.