Fog computing is a new way of using computers that builds on standard cloud technology by taking advantage of the resources at the user's location to improve services. Because of its advantages, which include cheaper operational costs, better security, and lower network latency, it is the most often used option for many real-time applications. However, since each fog device is different, it is hard to figure out how to divide up and schedule resources. This paper proposes a novel approach for resource allocation and scheduling in a fog computing environment using a crow search mechanism and a multi-objective population-based evolutionary mechanism. The proposed work considers two goals: the security hit rate and the success rate. These two aims must be met to the greatest extent possible. A local search method improves how well the crow search algorithm (CSA) works. The suggested work uses the evolutionary method to figure out how to schedule and divide resources in a fog computing environment. We have contrasted the effectiveness of our suggested hybrid CSA with two other approaches, the original CSA and the Genetic Algorithm (GA), based on two metrics: success rate and security hit ratio. We have used seven different scenarios with different values for the parameters. We found that our proposed hybrid CSA outperformed other currently employed methods. The study's outcome also shows how well the suggested algorithm accomplishes its objectives.