In recent years, Location-Based Services (LBSs) have received a lot of attentions due to a wide range of potential applications. In such mobile applications and services, the travel recommendation system is one of popular LBSs for mobile users. More and more research studies focus on discovering of various kinds of mobile patterns from various mobile databases. In which, one of popular research issues is mining of the travel trajectory patterns from mobile travel trajectory databases. Mining travel knowledge from travel trajectory data benefits for supporting of an intelligent travel recommendation system. However, it is not easy to obtain the real travel trajectory data for mobile users since the problem of personal privacy. To develop a travel trajectory data generator is very useful for the research communities on mobile travel behavior mining. In this paper, we present a simulation framework to generate mobile travel trajectory data based on the statistics and observations of real travel information. In the simulation model, we generate not only the travel trajectories for mobile users but also the mobile transactions for each arrived attraction according to the reasonable probability designs.