In this paper, we propose a two-layered parking lot management system for charging scheduling of electric vehicles (EVs) considering a realistic vehicular mobility pattern. EVs are categorized in two groups based on their mobility patterns: Regular EVs and Irregular EVs. We use the data from an vehicular mobility trace collected from the Canton of Zurich for the regular EVs and a probabilistic pattern built on top of this Zurich trace aiming at modeling the behavior of irregular EVs. To the extend of our knowledge, this is the first EV charging scheduling study in the literature that utilizes a big-scale realistic vehicular mobility trace. The performance of the proposed system is compared with well-known routine unaware scheduling mechanisms (e.g., First Come First Serve) with regard to maximizing the parking lot revenue. Our results show that, our proposed system outperforms well-known routine unaware scheduling mechanisms and it is evident that real environments would benefit from using such parking lot management systems in Smart Cities.