One of the popular networks highly used for creating various Adhoc network applications is Mobile Ad hoc Networks, which are vulnerable to various security attacks, one of which is the blackhole attack. One of the networks that come under MANET is the Vehicular Adhoc network. It uses multi-hop data transmission, which provides various pathways to malicious attacks. One of the attacks, non-identifiable easily, is a blackhole attack, a category of DoS attack. Earlier research methods provided different algorithms for identifying and detecting individual attacks or standard security methods. At the same time, the accuracy of malicious activity detection and elimination is not up to the mark. In which a malevolent node misleadingly publicizes itself as having the shortest path to a destination, causing other nodes to send their data to it, which the attacker discards. This paper proposes a genetic algorithm-based approach for detecting blackhole attacks in VANETs. Our approach uses a combination of network metrics, such as network throughput and end-to-end delay, and genetic algorithms to identify malicious nodes. The genetic algorithm is used to optimize the selection of network metrics and determine the weights given to each metric in the detection process. Simulation results show that our approach effectively detects blackhole attacks with high accuracy and low false positive rates.