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Bank bailouts are not “one-shot” events, but rather dynamic processes that occur over phases that often last for months or years. Regulators “catch” financially-distressed banks with declining capital ratios and provide financial assistance. At this time, regulators also “restrict” banks’ activities for ex ante unknown lengths of time until “release” when bank capital ratios reach sufficiently healthy levels. This “catch-restrict-release” bailout approach is employed globally, including the US TARP in 2008 and subsequent bank bailouts across EU nations. This approach applies to both of the major bailout methods–capital injections (CIs) and debt guarantees (DGs). We offer design principles for socially-optimizing regulators that choose “catch, restrict, and release” plans in advance for both CI and DG bailout methods. These are tailored to each individual bank’s characteristics and conditions to maximize social welfare. Regulators also select between CI and DG, choosing whichever bailout method that yields a higher expected social welfare value. In the model, banks maximize their individual values by dynamically managing their capital in anticipation of these regulatory plans, as well as any subsequent responses after they may have been bailed out. We conduct tests of model predictions using hand-collected bank bailout data from multiple EU nations. Findings suggest regulators bail out banks in a qualitatively consistent fashion with model predictions for social optimization.