There is increasing interest in designing and manufacturing high-performance batteries for a wide variety of applications;however, finding the best electro-chemical system and configuration is a challenging task given all the possibilities.In particular, choosing an electrolyte optimal for a given battery configuration and chemistry is a daunting and crucial task in and of itself. We propose a computational approach to electrolyte screening and selection for a Li-air battery.Li-air batteries are particularly attractive for mobile and transportation applications given their high theoretical volumetric and gravimetric energy densities, which in part is due to using environmental oxygen.Through a combination of techniques, starting with \abinitio calculations to provide fundamental parameters, to molecular dynamics which, in turn, provides transport coefficients to a full-scale model battery, we can predict battery performance.Predictions using these techniques spanning many spatio-temporal scales are compared with a limited set of cyclic voltammetry experiments for validation before applying the process to a wide range of solvent, salt and other permutations to select the best candidates for intensive development.The concept of computational screening for battery development is not novel, eg the work the Ceder group, but here we focus on identifying ideal electrolytes for Li-air batteries where the diffusion of dissolved Li ions and molecular oxygen have been identified as power performance metrics.