Mainstream Vehicular Ad-hoc networks (VANETs) are driving itself to the new era of Internet of Vehicles (IoV) which forms a major component in the revolutionary Internet of Things (IoT). With the recent improvements in the field of communication, controls and embedded system; vehicles these days act as a moving sensor. These vehicles get the ability to sense the information in the environment and are able to broadcast it to other vehicles and the infrastructure around. Like many other peripherals of Internet of Things (e.g., Smart City), the IoV will have the communications, storage, and artificial intelligence to aid the vehicle driver. Attracting a number of researchers and industries to work on this particular field, IoV has turned into a supreme research area. In this paper the problem of broadcast scheduling among the vehicles is addressed. Since there is an extensive usage of broadcast communication between the connected vehicles, the issue of scheduling for TDMA based VANETs is investigated. The proposed methodology utilizes an evolutionary approach to deal with the Broadcast Scheduling Problem (BSP). A discrete Genetic algorithm (dGA) was chosen to solve the issue and improve the transmissions in optimal time slots with increased channel utilization. On comparing with other TDMA based algorithms as mentioned in the literature, the proposed dGA increases the number of transmissions by reducing the TDMA channel length. © BEIESP.