Sensors capable of detecting magnetic fields are widely applied in many areas of engineering. A magnetometer is a device that based on the use of a magnetic sensor is capable of measuring the magnitude and direction of a magnetic field. Magnetometers GMI are magnetic transducers which sensors elements are based on the Giant Magnetoimpedance effect (Giant Magnetoimpedance - GMI) that is characterized by large variation of the impedance (magnitude and phase) of a sample of ferromagnetic material when subjected to an external magnetic field. The magnetic transducers sensitivity is directly affected by the sensitivity of its sensor elements. In the case of GMI samples, the sensitivity is affected by several parameters, and this dependence is not well modeled quantitatively. This dissertation presents a computational model based on feedforward Multilayer Perceptron Neural Networks and Genetic Algorithms that determines the optimal impedance phase sensitivity of the GMI effect, as functions of the magnetic field, for Co70 Fe5 Si15 B10 ferromagnetic amorphous alloys, The proposed model is based on some of the main parameters that affect it: length of the samples, DC level and frequency of the excitation current and the external magnetic field.