Turbocharging has become the favored approach for downsizing internal combustion engines to reduce fuel consumption and CO2 emissions, without sacrificing performance. Matching a turbocharger to an engine requires a balance of various design variables in order to meet the desired performance. Once an initial selection of potential compressor and turbine options is made, corresponding performance maps are evaluated in 1D engine cycle simulations to down-select the best combination. This is the conventional matching procedure used in industry and is ‘passive’ since it relies on measured maps, thus only existing designs may be evaluated. In other words, turbine characteristics cannot be changed during matching so as to explore the effect of design adjustments. Instead, this paper presents an ‘adaptive’ matching methodology for the turbocharger turbine. By coupling an engine cycle simulation to a turbine meanline model (‘in-the-loop’), adjustments in turbine geometry are reflected in both the exhaust boundary conditions and overall engine performance. Running the coupled engine-turbine model within an optimization framework, the optimal turbine design evolves. The methodology is applied to a Renault 1.2 L turbocharged gasoline engine, to minimize fuel consumption over given full- and part-load operating points, while meeting performance constraints. Despite the current series production turbine being a very good match already, and with optimization restricted to a few turbine geometric parameters, the full-load case predicted a significant cycle-averaged BSFC reduction of 3.5 g/kWh, while the part-load optimized design improved BSFC by 0.9 g/kWh. No engine design parameters were changed, so further efficiency gains would be possible through simultaneous engine-turbocharger optimization. The proposed methodology is not only useful for improving existing designs; it can also develop a bespoke turbine geometry in new engine projects where there is no previously available match. For these reasons, ‘adaptive’ turbo matching will become the standard approach in the automotive industry.