Automatic Speech Recognition systems are powerful tools that transform speech into text, allowing the possibility of turning spoken requests into actions. However, these tools are susceptible to adversarial attacks that can have dramatic consequences. It is therefore of paramount importance to expand the knowledge on the weaknesses of these models. In this context, our contribution is twofold. First, we introduce Speech-EA, an evolutionary algorithm designed to work as a black-box attack against ASR systems for the untargeted scenario. Second, we experimentally validate Speech-EA against Wav2Vec 2.0. With 10, 000 attack runs on 1, 000 audio samples taken from the Synthetic Speech Command Dataset, our attack achieves a success rate of \(89.9 \%\) . It creates an adversarial audio in 13.82 seconds on average, with many completed in less than 2 seconds. A human evaluation confirms their high acoustic quality (audio indistinguishability in \(59 \%\) of cases; adversarial audio heard as representing the intended clean command in \(88.3 \%\) of cases). These results demonstrate that Speech-EA is effective, competitive, fast and highlights the need for more robust ASR systems.