Neuronal morphology plays a crucial role in the information processing capabilities of neurons. Despite the importance of morphology for neural functionality, biological data is scarce and hard to obtain. Therefore, virtual neurons are devised to allow extensive modelling and experimenting. The main problem with current virtual-neuron generation methods is that they impose severe a priori constraints on the virtual morphologies. These constraints are based on widespread assumptions and beliefs about the morphology of real neurons. To overcome this problem, we present EvOL-Neuron, a new method based on L-Systems and Evolutionary Computation that imposes a posteriori constraints on candidate virtual neuron morphologies. As a proof of principle, our experiments show the power of the new method. Moreover, our method revealed a limitation in the description of neural morphology in the literature. We empirically show that Hillman's fundamental parameters of neuron morphology are satisfactory but not sufficient to describe neuronal morphology. The results are discussed and an outline for future research is given. We conclude that we succeeded in devising a new method for virtual-neuron generation that does not impose a priori limitations on the virtual-neuron morphology.