GMI sensors are a new family of magnetic sensors that exhibit a huge variation of their impedance when subjected to external magnetic fields. These sensors have been used in the development of high sensitivity magnetometers, aimed at measuring ultra-weak magnetic fields. In turn, the sensitivity of a magnetometer is directly associated with the sensitivity of their sensor elements. In the case of GMI samples, this sensitivity is optimized by maximizing the variation of the impedance magnitude or phase as a function of the magnetic field applied to the sample. Recent studies show that GMI transducers based on phase variation can exhibit sensitivities up to 100 times higher than those presented by transducers based on impedance magnitude readings. The results obtained in these previous studies made the current work focusing on the maximization of phase sensitivity, which is mostly dependent on four factors: sample length, external magnetic field, DC level and frequency of the excitation current. However, the search for the set of parameters that optimizes the sensitivity of the samples is usually empirical and very time consuming. Thus, this dissertation proposes a new optimization technique, based on the use of genetic algorithms evolving on hardware, in order to define which set of parameters is responsible for maximizing the sensitivity of the samples. It should be noted that in addition to the optimization parameters previously described, this work also carried out tests considering the amplitude of the excitation current as a free variable, and the results obtained are presented and discussed. A test bench was implemented and a graphical interface was developed in LabVIEW to monitor and measure the impedance behavior of GMI samples due to variations in the parameters of interest. In turn, a Matlab optimization module based on genetic algorithms was implemented, in order to find the combination of parameters that maximizes the impedance phase sensitivity of the evaluated GMI sensors (optimum operating point).