In this paper we developed an approach based on the use of a finite element model combined with the use of genetic algorithms with which it was possible, following an initial calibration of the model of the healthy structure, to identify and localize the damage on the case study under analysis. The FEM Updating process is commonly used to obtain a tuning between the numerical model and the experimental model of a structure, in this case it is proposed to be used to identify the damage on the structure. The calibration phase requires the frequencies and modal shapes of the structure, which are obtained from the accelerometer data using the Covariance-Driven Stochastic Subspace Identification (SSI-COV) method. The genetic algorithm is run with the aim of finding the best configuration of the parameters governing the physical model of the structure: these parameters can be either material characteristics such as Elastic Modulus and Poisson’s Coefficient, or cross-sectional characteristics such as Area and Inertia. The chosen objective function employs both frequencies and experimental modal forms appropriately weighted based on previous tuning. The case study used to test the previously mentioned procedure was retrieved from an open dataset of the Leibniz University Hannover: a steel lattice tower of approximately 9 m subjected to monitoring, using accelerometers, for several months and on which reversible damage mechanisms were induced. The techniques we developed were able not only to achieve tuning between the numerical and experimental models but also to automatically identify and locate the damage scenarios induced on the structure. The FEM Updating process proposed here is compatible with all major structural numerical modelling software, which makes it extremely flexible and adaptable to further real-world cases such as bridges and viaducts.