Optimal design methods for nonlinear models are dependent on the true but unknown parameter values. Criteria for developing designs that are robust to the choice of parameter values such as ED optimality have been proposed. However, these criteria are computationally intensive and can perform poorly at extremes of the prior parameter distribution. Two different criteria are proposed. Both involve evaluation of the determinant of the Fisher information matrix over models formed at various combinations of the 2.5th and 97.5th percentiles of the parameter space. The performance of the proposed optimality criteria is compared to two existing robust optimal design criteria.