Multiscale systems defined on trees can provide local time and scale information about the behavior of the process in contrast to the usual time-domain model and Fourier transforms. Because the model predictive control (MPC) framework uses a model of the process to determine the optimal control action, improving the model by using a multiscale approach will result in controller actions that can compensate for phenomena that may occur at different scales. This work develops multiscale models on trees, describes how these time-scale models can be used in the MPC framework to represent both the process and the disturbance, and proposes a new optimization strategy to determine the controller actions such that the optimal inputs, at the finer scales reflect the inputs at the coarser scales. The performance of this multiscale MPC strategy is demonstrated on a continuous process and on a chemical batch reactor.