The identification of model material parameters is often required when assessing existing structures, in damage analysis and structural health monitoring. A typical procedure considers a set of experimental data for a given problem and the use of a numerical or analytical model for the problem description, with the aim of finding the material characteristics which give a model response as close as possible to the experimental outcomes. Since experimental results are usually affected by errors and limited in number, it is important to specify sensor position(s) to obtain the most informative data. This work proposes a novel method for optimal sensor placement based on the definition of the representativeness of the data with respect to the global displacement field. The method employs an optimisation procedure based on Genetic Algorithms and allows for the assessment of any sensor layout independently from the actual inverse problem solution. Two numerical applications are presented, which show that the representativeness of the data is connected to the error in the inverse analysis solution. These also confirm that the proposed approach, where different practical constraints can be added to the optimisation procedure, can be effective in decreasing the instability of the parameter identification process.