Automatic target tracking in forward-looking infrared (FLIR) imagery is a challenging research area in computer vision. This task could be even more critical when real-time requirements have to be taken into account. In this context, techniques exploiting the target intensity profile generated by an intensity variation function (IVF) proved to be capable of providing significant results. However, one of their main limitations is represented by the associated computational cost. In this paper, an alternative approach based on genetic algorithms (GAs) is proposed. GAs are search methods based on evolutionary computations, which exploit operators inspired by genetic variation and natural selection rules. They have been proven to be theoretically and empirically robust in complex space searches by their founder, J. H. Holland. Contrary to most optimization techniques, whose goal is to improve performances toward the optimum, GAs aim at finding near-optimal solutions by performing parallel searches in the solution space. In this paper, an optimized target search strategy relying on GAs and exploiting an evolutionary approach for the computation of the IVF is presented. The proposed methodology was validated on several data sets, and it was compared against the original IVF implementation by Bal and Alam. Experimental results showed that the proposed approach is capable of significantly improving performances by dramatically reducing algorithm processing time.