Government regulations for fuel economy and emission standards have driven the development of technologies that improve engine performance and efficiency. These technologies are enabled by an increased number of actuators and increasingly sophisticated control algorithms. As a consequence, engine control calibration time, which entails sweeping all actuators at each speed-load point to determine the actuator combination that meets constraints and delivers ideal performance, has increased significantly. In this work we present two adaptive optimization methods, both based on an indirect adaptive control framework, which improve calibration efficiency by searching for the optimal process inputs without visiting all input combinations explicitly. The difference between the methods is implementation of the algorithm in steady-state vs dynamic operating conditions. The goal of this work is to study the optimization performance tradeoffs between robustness to sensor noise and required optimization time. We demonstrate both methods on an engine installed in a dynamometer where multiple actuators were calibrated to minimize Break Specific Fuel Consumption (BSFC), while satisfying input and output constraints.