The aim of this paper is to present a biologically inspired Neuro Evolutive Algorithm (NEA) able to generate modular, hierarchical and recurrent neural structures as those often found in the nervous system of live beings, and that enable them to solve intricate survival problems. In our approach we consider that a nervous system design and organization is a constructive process carried out by genetic information encoded in DNA. Our NEA evolves Artificial Neural Networks (ANNs) using a Lindenmayer System with memory that implements the principles of organization, modularity, repetition (multiple use of the same sub-structure), hierarchy (recursive composition of sub-structures), as a metaphor for development of neurons and its connections. In our method, this basic neural codification is integrated to a Genetic Algorithm (GA) that implements the constructive approach found in the evolutionary process, making it closest to the biological ones. Our method was initially tested on a simple, but non-trivial, XOR problem. We also submit our method to two other problems of increasing complexity: Prediction of the effect of a new drug on Breast Cancer and KDDCUP'99 benchmark intrusion detection dataset. Some advantages of the proposed methodology are that it increases the level of implicit parallelism of the GA and seems to be capable of generating minimal satisfactory neural architectures, resulting in a reduction of project costs and increasing the performance of the evolved ANN, suggesting a promising potential for future applications.