In this paper, we propose a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal searching behaviour and group living theory. The algorithm is based on the Producer-Scrounger model, which assumes group members search either for 'finding' (producer) or for 'joining' (scrounger) opportunities. Animal scanning mechanisms (e.g., vision) are incorporated to develop the algorithm. We also employ 'rangers' which perform random walks to avoid entrapment in local minima. When tested against benchmark functions, GSO outperformed competitively with other evolutionary algorithms in terms of accuracy and convergence speed on most of the benchmark functions.