Nowadays, tourists are planning trips by their own using the available services on the web. Travelling individually does not have the pleasure of group tours, so some tourists decide to find similar friends on their destination to share the joy of tours and trips. Also, they may not be familiar with the routes available in the city. In this paper, a mobile application is designed, developed, implemented and evaluated to recommend similar tourists to each other and generate optimal route based for each individual. This system is integrating mobile GIS, recommender systems and artificial intelligence tour planning algorithms into a single application. Being on a mobile platform is necessary, especially for navigation and tour planning systems. User may have to carry his cellphone in order to follow the suggested tour. The recommender system is a demographic filtering recommender to find the similar tourists. It considers age, nationality, education and interest to find the proper matches. After suggesting similar tourists to each other, one of the versions of Ant Colony Optimization namely Ant System is used to plan the optimal tours for each individual. This problem can be defined as a new version of Multiple Travelling Salesman Problem with mutual nodes to visit simultaneously. Different tourists decide to share a tour that stay in different places. This system should find a proper sequence of sightsees to minimize the length of the tour and also it should maximize the number of mutual streets in a tour to let tourists travel together during the tour. The objective function is to minimize the sum of the distances each tourist traversed and to maximize the number of mutual streets with a proper POI sequence. Maximizing number of mutual streets was considered because tourists want to travel as a group in a shared trip. Implementation of the system took place in Shiraz, Iran. Shiraz is the fifth-most-populous city of Iran and the capital of Fars Province with more than 4000 years of history that made it one of the key tourism sites in Iran. Numbers of questionnaires were distributed among different people to evaluate the people recommendation system. In addition, tour planning algorithm was evaluated using a well-known evolutionary algorithm (i.e. Genetic Algorithm). Mean, standard deviation and the convergence graphs of the two mentioned algorithms were compared. Results indicated accurate performance of the recommender system and high accuracy and precision of the route planning algorithm. Recommender System had a mean difference of 0.2 with the questionnaire results, which indicated its good functionality. Ant System reached the minimum value of the objective function (34.70) with a better standard deviation (0.61) compared to Genetic Algorithm. However, Genetic Algorithm performed better in mean value of the tests (34.32) which is a measure of the precision. Convergence graph of the Genetic Algorithm showed a fast convergence with lower objective values in the beginning. Ant System convergence graph showed a smooth convergence toward the optimal solution with an initial population with higher objective values. This indicated the proper functionality of the Ant System and the possibility of improving the results with generating better initial population.