This paper proposes a min-max Economic Model Predictive Control approach for discrete time uncertain systems: a MPC min-max strategy where the worst-case performance with respect to uncertainties is optimized. Unfortunately, many min-max MPC formulations yield intractable optimization problems with exponential complexity, for this reason a min-max algorithm for a certain type of model uncertainty is derived in this paper. The transformation of the original problem into a second-order cone program is the most remarkable feature meaning that the min-max problem is written as a convex program. The result is an optimization problem with polynomial complexity.