A possible alternative to fine topology tuning for Neural Network (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely randomly connected neurons. The promises of ESNs have been fulfilled for supervised learning tasks, but unsupervised learning tasks, such as control problems, require more flexible optimization methods. We propose here to apply state-of-the-art methods in evolutionary continuous parameter optimization, to the evolutionary learning of ESN. First, a standard supervised learning problem is used to validate our approach and compare it to the standard quadratic one. The classical double pole balancing control problem is then used to demonstrate that unsupervised evolutionary learning of ESNs yields results that compete with the best topology-learning methods.