This paper presents a new method for solving the traditional vehicle routing problem based on real-time data. The proposed approach starts with collecting and analyzing real time data from established sensors or data entered manually to make sure this data represent the real time status for routes and edges then selecting the most influential factors (route width ,car flow ,car speed and average number of cars that move in this rout in specific slot of time) that effect directly in classifying the available routes. Based on this selection, a comparison is made between different routes and the best one is selected accordingly. The approach is an iterative process that is repeated in order to obtain the global optimum. Experimental results show that the proposed approach has the potential to obtain global solutions that are optimal or more accurate because it affected by the real time data so it reflects the real status of the rout in real time than other evolutionary methods.