Electric vehicle penetration is increasing around the world. Due to the random nature and the size of batteries in the vehicles, they impose a significant impact on the electric grid if they are not managed effectively. The objective of this project is to increase the prediction accuracy of the electric vehicle charging load. This work will incorporate electric vehicle driving patterns and consumer behavior. The agent-based modeling approach is considered in this work to predict the charging load at the residential level. An open-source tool, NetLogo, is used to model the consumer behavior and driving patterns of individual vehicles. The following random behaviors are considered in this work: the amount of charge at arrival, vehicle availability for charging, frequency of charging, travel purpose, geographical impact, and willingness to change their behavior. This model will help utility electric utilities plan for large penetration of electric vehicles. Companies such as Evergy will be able to use this information to better supply and distribute electricity to Kansans.