An astronomical survey’s observing strategy, which encompasses the frequency and duration of visits to each portion of the sky, impacts the degree to which its data can answer the most pressing questions about the universe. Surveys with diverse scientific goals pose a special challenge for survey design decisionmaking; even if each physical parameter of interest has a corresponding quantitative metric, there’s no guarantee of a “one size fits all” optimal observing strategy. While traditional observing strategy metrics must be specific to the science case in question, we exploit a chain rule of the variational mutual information to engineer TheLastMetric, an interpretable, extensible metric that enables coherent observing strategy optimization over multiple science objectives. The upcoming Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) serves as an ideal application for this metric, as many of its extreagalactic science goals rely upon purely photometric redshift constraints. As a demonstration, we use the LSST Metrics Analysis Framework (MAF) to quantify how much information about redshift is contained within photometry, conditioned on a fiducial true galaxy catalog and mock observations under each of several given observing strategies, generated by the LSST Operations Simulator (OpSim). We compare traditional metrics of photometric redshift performance to TheLastMetric and interpret their differences from the perspective of observing strategy optimization. Finally, we illustrate how to extend TheLastMetric to cosmological constraints by multiple probes, jointly or individually.