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The development of optically transparent, electrically conductive oxides (TCOs) is relevant to a broad range of optoelectronic applications. The design of new TCOs and the optimization of existing ones require powerful modeling tools and algorithms that would be able to handle the multi-objective nature of the problem. Here we present a new approach that merges TCO physics to multi-objective evolutionary optimization algorithms. In contrast to conventional figure-of-merit relations, our approach discards any weighting-induced bias and hence provides accurate information on the material's performance. Moreover, it allows identifying optimization guidelines for many TCO structures. We show that once we envisage a device application (e.g. thin film solar cells) the analysis of the calculated Pareto-optimal set by data clustering techniques yields essential information on performance enhancement. Our approach opens the way towards multicriteria design of optoelectronic materials and devices.