As the use of unmanned aerial vehicles (UAVs) for military and civilian purposes increases, UAV route planning problem has gained importance. The problem varies according to factors like the number and properties of UAVs, the characteristics of the terrain and the objective functions used; and different approaches have been developed for it in the literature. This study considers route planning for a fleet of homogeneous UAVs that need to collect information from target points with different levels of importance before returning to the base. The two objectives used are minimizing the total distance traveled and maximizing the total importance level of the targets visited, and alternative efficient solutions are generated rather than a single solution. The problem is solved with mathematical modelling and genetic algorithm approaches, and computational tests are made with different number of UAVs and target points. Solutions from different regions of the objective space could be obtained to be presented to the decision makers by both methods. Also, with the genetic algorithm, solutions close to the exact solutions could be obtained in considerably shorter computation times.