The exceptional quality of diffusion based generative models has garnered a wide audience; however, the computational burden of their iterative sampling procedure can make deployment of these models cumbersome. Speeding up diffusion sampling is an area of active research that has produced new samplers, schedules, and even new models. We show that Bayesian Optimization is an efficient approach for tuning these sampling parameters and recommend its adoption as a means to optimize diffusion sampling for a specific task or objective. We show that we can achieve comparable and oftentimes better performance using less generations than alternative approaches on various image-related tasks.