Testing of applications that use data from e-Government services as input requires test data that is real-life like but where the privacy of personal information is guaranteed. Many approaches exist for creating high-quality synthetic test data, but most of them need real-life raw data as input. Our research aims to develop and evaluate an approach for generating and evolving real-life like synthetic test data without using real-world raw data. The expected benefit of our research is to enable the creation and evolvement of meaningful and real-life like synthetic test data in situations where real-life raw data is not accessible due to privacy reasons.