The Implementation of a Decision Support System for Human Resources evaluation and Selection is used to evaluate twenty-five CVS and select the best three among them from linked in to decrease the human interaction with the employment process which may increase the accuracy of employees selecting. The aim of this study is to develop Implementation of a Decision Support System for Human Resources Evaluation and Selection. In this study, the researcher seeks for the appropriate staff for the project according to their skills and characteristics using Artificial Intelligence techniques. In addition, the objectives of this study are studying the previous approaches to define or modify a method for solving its problem by performing an evaluation study to see the effectiveness of our defined method. The study major contribution is the suggestion an ontology that focuses on defining the competencies and experience for workers using the Genetic Algorithm to use the classification rule, and the use of semantic methods in the current thesis, it is the maybe of the one attempt to create a method that selects the right staff according to their skills and expertise without the intervention of the human factor. To use this system in different companies to reduce time and effort in the selection process. However, any company will be more productive in its various businesses because of choosing the right employee in the right place. The program was created by using C# and Asp.net. The assessment of the classifier accuracy of the resulting classification rule indicates the requiring to create more precise descriptions to improve the SRL. In the present study, the researcher introduced a semantic-based method capable of recommending the workers that apply for job by examining the experiences, skills, major and. The plan achieved impressive outcomes via an F-Measurement value of 0.7747 and Precision Value of 0.7942. Keywords: Human resources, Ontology, Semantic Index, semantic annotation, Genetic algorithm