Neural networks can help in model development, and it has been done. So "never" is false. I guess those who say NNs don't work either don't know how to use them, or tried to use them with wrong data and/or incorrectly. NN is just a tool and before you can use it you have to understand the math and statistics behind them, at least in abstract fashion.
While neural networks can be trained by genetic algorithms, they are much more often trained by some other, more direct procedure (gradient descent, for instance). The important thing is to understand that genetic algorithms, in themselves, do not "learn" anything- they are optimizers of other systems. To say that genetic algorithms are "better" some how than neural networks is like saying that saws are "better" than hammers!
Genetic algorithms can also simply build neural networks. In that case neural network structure does not depend on generic algorithm but on sequence of examples that were used by the generic algorithm during training.