We present in this paper a new approach for polyphonic music transcription using evolution strategies (ES). Automatic music transcription is a complex process that still remains an open challenge. Using an audio signal to be transcribed as target for our ES, information needed to generate a MIDI file can be extracted from this latter one. Many techniques presented in the literature at present exist and a few of them have applied evolutionary algorithms to address this problem in the context of considering it as a search space problem. However, ES have never been applied until now. The experiments showed that by using these machines learning tools, some shortcomings presented by other evolutionary algorithms based approaches for transcription can be solved. They include the computation cost and the time for convergence. As evolution strategies use self-adapting parameters, we show in this paper that by correctly tuning the value of its strategy parameter that controls the standard deviation, a fast convergence can be triggered toward the optima, which from the results performs the transcription of the music with good accuracy and in a short time. In the same context, the computation task is tackled using parallelization techniques thus reducing the computation time and the transcription time in the overall.