Improving efficiency, reliability and availability of gas turbines have become more than ever one of the main areas of interest in gas turbine research. This is mainly due to the stringent environmental regulations that have to be met in such a mature technology sector; and consequently new research challenges have been identified. One of these involves the establishment of high fidelity, accurate, and computationally efficient engine performance simulation, diagnosis and prognosis technology. Performance prediction of gas turbines is strongly dependent on detailed understanding of the engine component behaviour. Compressors are of special interest because they can generate all sorts of operability problems like surge, stall and flutter; and their operating line is determined by the turbine characteristic. Compressor performance maps, which are obtained in costly rig tests and remain manufacturers proprietary information, impose a stringent limitation that has been commonly resolved by scaling default generic maps in order to match the targeted off-design or engine degraded measurements. This approach is efficient in small range of operating conditions but becomes less accurate for a wider range of operations. In this paper, a novel compressor map generation method, with the primary objective of improving the accuracy and fidelity of the engine model performance prediction is developed and presented. A new compressor map fitting and modelling method is introduced to simultaneously determine the best elliptical curves to a set of compressor map data. The coefficients that determine the shape of compressor maps' curves have been analyzed and tuned through a multi-objective optimization algorithm in order to meet the targeted set of measurements. The proposed component map generation method is developed in the object oriented Matlab/Simulink environment and is integrated in a dynamic gas turbine engine model. The accuracy of this method is evaluated for off-design steady state and transient engine conditions. The proposed compressor map generation method has the capability to refine current gas turbine performance prediction approaches and to improve model-based diagnostic techniques.