Designing amino acid sequences that are stable in a given target structure amounts to maximizing a conditional probability. A straightforward approach to accomplish this is a nested Monte Carlo where the conformation space is explored over and over again for different fixed sequences. In this paper we discuss an alternative Monte Carlo approach, multisequence design, where conformation and sequence degrees of freedom are simultaneously probed. The method is explored on hydrophobic/polar models. A statistical analysis of sequence correlations is also discussed. It is found that hydrophobic/polar model sequences and enzymes display hydrophobicity correlations that are qualitatively similar.