Characterization is important for making models match reality and allowing for quick and accurate measurements of parameters. In this paper we present a method for designing dynamic battery experiments using an evolutionary algorithm that directly generates Pareto fronts via differential evolution. This optimization creates current trajectories for multiple objectives, namely, maximizing Fisher information gathered while minimizing battery damage. An estimator is used on simulated battery experiments to verify the improvements associated with these trajectories. This exercise illustrates the experimental trade-offs between gathering parameter information and causing battery degradation. The procedure in this paper is widely applicable as both the battery model and parameter's of interest can be substituted as needed.