The simulation program GTperform is used to estimate the machine settings from performance measurements for the gas turbine model STG-800 at Siemens Industrial Turbomachinery in Finspång, Sweden. By evaluating different settings within the program, the engineers try to estimate the one that generatesthe performance measurement. This procedure is done manually at Siemens and is very time-consuming. This project aims to establish an algorithm that automatically establishes the correct machine setting from the performance measurements. Two algorithms were implemented in Python: Simulated Annealing and Gradient Descent. The algorithms analyzed two possible objective functions, and objective were tested on three gas turbines located at different locations. The first estimated the machine setting that generated the best fit to the performance measurements, while the second established the most likely solution for the machine setting from probability distributions. Multiple simulations have been run for the two algorithms and objective functions to evaluate the performances. Both algorithms successfully established satisfactory results for the second objective function. The Simulated Annealing, in particular, established solutions with a lower spread compared to Gradient Descent. The algorithms give a possibility to automatically establish the machine settings for the simulation program, reducing the work for the engineers.