Delivering software in an incremental fashion implicitly reduces many of the risks associated with delivering large software projects. However, adopting a process, where requirements are delivered in releases means decisions have to be made on which requirements should be delivered in which release. This paper describes a method called EVOLVE+, based on a genetic algorithm and aimed at the evolutionary planning of incremental software development. The method is initially evaluated using a sample project. The evaluation involves an investigation of the trade-off relationship between risk and the overall benefit. The link to empirical research is two-fold: Firstly, our model is based on interaction with industry and randomly generated data for effort and risk of requirements. The results achieved this way are the first step for a more comprehensive evaluation using real-world data. Secondly, we try to approach uncertainty of data by additional computational effort providing more insight into the problem solutions: (i) Effort estimates are considered to be stochastic variables following a given probability function; (ii) Instead of offering just one solution, the L-best (L>1) solutions are determined. This provides support in finding the most appropriate solution, reflecting implicit preferences and constraints of the actual decision-maker. Stability intervals are given to indicate the validity of solutions and to allow the problem parameters to be changed without adversely affecting the optimality of the solution.