This article presents a novel direct search method of optimization, Game of Patterns (GoP) method, so-called by the author, for solving unconstrained mixed integer nonlinear optimization problems. The GoP method is based on: a set of η random patterns search, which initially forms the set of η active players, namely, {H_[0]1,...,H_[0]η}; and a game rule framework. At each κth game round, each pth player H_[k]p will explore inside his own pattern in the current κth round, if the player is active. The strategy of each pth player is given by a random quantity S_[k]p, which will allow each player to explore inside his own pattern by a set of trial  S_[k]p points. At the beginning of each κth game round, each active pth player bets  according to his budget M_p for each round. At the end of each κth round, the player that had identified the best objective function value is considered the winner of the κth round, therefore the rest of active players must pay off the winner their bets. This process is recurrently repeated until that had been disqualified  players from the game. It is worthwhile to point out that any player will be disqualified if his balance account  is less than his budget  for each round.