The predictive monitoring of business processes is a part of process mining that is concerned with the prediction of process indicators of running process instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. Many predictive monitoring proposals obtain these predictions using predictive models generated by machine learning algorithms. In this paper, we present an approach for predictive monitoring based on the use of evolutionary algorithms. Specifically, our method returns a model which consists in a set of decision rules that predicts the value of a process indicator according to the features of the event log, for a determined running process instance. An advantage of this approach is that the generated rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, we have developed a full software stack to support our approach that includes a ProM plugin to support the training phase and a framework that enables the integration of run-time predictions with business process management systems. Obtained results show the validity of our proposal for two real-life datasets (BPI Challenge 2013 and IT Department of Andalusian Health Service, SAS).