We develop a general model for optimal recovery planning for an endangered species that seeks to satisfy delisting criteria at minimum economic cost. We apply our model to the management of the California condor (Gymnogyps californianus), a critically endangered species under threat from lead poisoning. Given the threats currently facing the condor, state and local governments have begun to regulate the use of lead ammunition within the condor's habitat. Currently, there is a significant amount of uncertainty regarding the effectiveness of the proposed regulations in reducing lead levels in the environment. An optimal recovery plan minimizes discounted expected costs over a specified time horizon. We numerically solve for optimal recovery plans for single and multiple managed subpopulations with infinite and finite time horizons. The optimal recovery plan is sensitive to the form and parameterization of the objective function. Solving model formulations with unique variants of the objective function generates a set of recovery plans that vary in probability of delisting and expected management cost. We present a discrete-time, stochastic model for the management of multiple subpopulations of an endangered species to achieve multiple, predetermined delisting criteria. Numerically solving the model results in recovery plans that optimally allocate resources across multiple subpopulations of a listed species in an attempt to achieve exogenous delisting criteria. We apply the model to the recovery of the California condor and present the results from multiple case studies, 1) two identical subpopulations and a single delisting criterion, 2) two spatially distinct subpopulations and a single delisting criterion, and 3) two subpopulations with two delisting criteria. We show that in the two subpopulation model with a single delisting criteria, optimal recovery plans allocate resources solely to the subpopulation with the larger population level in the current time period, and abandon the lower population to extinction. Captive breeding populations provide insurance against existing threats (and potential shocks) to wild subpopulations, and a source of potential translocations to augment wild subpopulations. We explore entry/exit decisions faced by planners when simultaneously managing both wild and captive populations of the California condor. Previous work on the optimal management planning for the California condor assumes that translocation costs are variable. However, it may be more realistic to model the cost of captive breeding populations as an annual fixed cost, where the cost does not depend on the number of individuals produced. Our model determines the conditions for `exiting' or ceasing captive breeding operations for a finite recovery planning horizon. We explore multiple case studies, including recovery planning when the state space is completely observable and under mixed observability. Simulation analysis is used to illustrate how recovery plans are implemented over time and to highlight the tradeoffs between management intensity and probability of achieving delisting criteria.