Using soft materials to build robots is becoming increasingly popular as the importance of morphological complexity in robot design becomes apparent. Additionally, physics simulators which are differentiable are increasingly being used to optimize robot morphologies and controllers over e.g. evolutionary algorithms due to the computational efficiency of gradient descent. One of the most commonly used methods to simulate soft materials is the Material Point Method (MPM), and soft roboticists have implemented the MPM in differentiable robotics simulations and successfully transferred their optimized designs to the real world, validating this approach for real-world soft robot design. However, choosing parameters for MPM that render it stable in a differentiable simulator are non-obvious. For this reason, here we introduce for the first time a set of best practices for employing the MPM to design and optimize soft robots using differentiable physics engines. We perform grid searches over many of the parameters involved in MPM to determine simulation stability ranges and performant parameter choices for a displacement task. This will allow newcomers to MPM simulation to rapidly iterate to find parameters for their application.