We propose a global index of impact based on the relative vulnerability of the local population of every species and the further application of regression trees globally optimized with evolutionary algorithms to study the fishing impact on the cartilaginous fish in southeastern Spain. The fishing impact is much higher in areas of less than 40 m depth within 11 km of the Cape Palos marine reserve. The impact also depends on the state of the sea and the kind of habitat. Deep-sea habitats associated with hard substrata and sandy beds show the highest impact, and sublittoral muds and habitats associated with circalittoral rocks with moderate energy show the lowest impact. The fishing impact changes throughout the moon cycle, showing different day-scale patterns associated with different habitats and different species compositions. Finally, we show that the global optimization of the regression trees can be essential to find some important patterns and that these trees are a useful tool for determining which areas are considered to be more important in terms of protection, taking into account specifically the vulnerability of the local populations.