This paper addresses the path planning problem of multiple unmanned aerial vehicles (UAVs). The paths are planned to maximize collected amount of information from desired regions (DRs), while avoiding forbidden regions (FRs) and reaching the destination. This study focuses on maximizing collected information instead of minimizing total mission time, as in previous studies. The problem is solved by a genetic algorithm (GA) with the proposal of novel evolutionary operators. The initial populations are generated from a seed-path for each UAV. The seed-paths are obtained both by utilizing the pattern search method and by solving the multiple-Traveling Salesman Problem (mTSP). Utilizing the mTSP solves both the visiting sequences of DRs and the assignment problem of which DR should be visited by which UAV?' All of the paths in the population in any generation of the GA are constructed using a dynamical UAV model. Simulations are realized in a MATLAB/Simulink environment for different mission scenarios and the results provide physically realizable flight paths, which visit DRs and avoid FRs. Real-world experiments are conducted by using small UAVs, which are constructed by autopilot integration on model airplanes. Flight tests performed based on simulated scenarios proved beneficial in maximizing the collected amount of information for multiple UAV missions.