The concave cost transportation problem (ccTP) is one of the practical distribution and logistics problems. The ccTP arises when the unit cost for transporting products decreases as the amount of products increases. Generally, these costs are modeled as nonlinear, especially concave. Since the ccTP is NP-hard, solving large-scale problems is time- consuming. In this paper, we propose a hybrid search algorithm based on genetic algorithms (GA) and ant colony optimization (ACO) to solve the ccTP. This algorithm, called hGACO, is a GA supplemented with ACO in where ACO is implemented to exploit information stored in pheromone trails during genetic operations, i.e. crossover and mutation. The effectiveness of hGACO is investigated comparing its results with those obtained by five different metaheuristic approaches given in the literature for the ccTP.