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The dramatic increase of vehicle use in urban situations, coupled with the short average length of the subsequent journey, leads to a large proportion of engine operation happening under cold start conditions. The cold start results in larger fuel consumption and higher emissions relative to a fully warmed engine, and ultimately the minimization of both of these has led to intense and complex calibration processes. Typically, the engine calibration is a quasi-static process requiring multidimensional sweeps across engine control variables to find the best combination of inputs for each steady-state load-speed operating condition. The use of optimal control techniques on appropriate engine models may reduce the search effort associated with calibration. This methodology is demonstrated here using a metric weighting thermal behavior and fuel use on low-order engine models with thermal dynamics to develop insights into the nature of the optimal control policies over the warm-up duration. These findings are validated using a numeric optimization performed on high-order engine simulation. Future optimization approaches, however, may be simplified by utilizing the analytic results. Subsequently, the trajectories for two different objective functions are compared with a production calibration to demonstrate the proposed methodology.