A framework for maximizing the robustness of nonlinear illposed inverse problems by choosing appropriate system inputs is presented. This framework is based on maximizing the lowest eigenvalues of the linearized and regularized nonlinear mapping. Stability and sensitivity of the eigenvalues of the linearization are studied. The results are applied to parameter estimation problems for elliptic partial differential equations. Numerical examples illustrating the results are given.