In Component-Based Software Engineering, the problem of selecting software components involve several risk factors. Traditionally, these have been identified and mitigated using software project management techniques. However, the new demand for intelligent components has added complexity to the process. Despite the success and technological advances, its development in an environment ready for production faces many challenges. There are numbers of technical issues that limit their adoption and selection, due to the introduction of new risk factors, different from traditional ones. Thus, our goal is to formulate a technique to minimize the risk in the intelligent component selection process. To achieve this goal, first, we review the literature to figure out how software project management takes care of the risk and to identify and classify the risks factor associated with intelligent software components. Second, we formulate the component selection problem as a search based optimization problem. And Third, we illustrate our proposal by presenting an example in the context of an air pollution forecasting component. As a result, we were able to: (1) identify a lack of useful tools to manage the risk factor effectively in a software project; (2) we classify intelligent component associated risk; and (3) we introduce a risk management technique that supports the component selection process by maximizing requirement accomplishment, that is, minimizing the risk of the provision of the functionalities that satisfy the requirements. Overall, our work is an initial step in using search-based optimization in risk management to the component selection process.