This study discusses the integrated order batching, sequencing and routing problem (IOBSRP) in warehouses. Distinguished from the past studies, a comprehensive nonlinear mixed integer optimization model is developed to simultaneously determine three decisions, including order batching, batch sequencing, and picker׳s routing, under the consideration of the minimum total tardiness of customer orders. The IOBSRP can be proven as a NP-Hard problem. Consequently, an algorithm integrating hybrid-coded genetic algorithm and ant colony optimization is developed to efficiently tackle the proposed nonlinear IOBSRP model. The hybrid-coded genetic algorithm is responsible for searching the near-optimal solutions of order batching and batch sequencing decisions by the hybrid-coded chromosome design and the evolutionary processes. For the picker routing decision of each batch, the ant colony optimization adopts the shortest path strategy to calculate the minimum of total travel time and its completion time. In order to exhibit the merits of the proposed algorithm, illustrative examples and sensitivity analysis were performed with various demands, batch capacity, and items per order. The experimental results show that the proposed hybrid algorithm has more advantage in the light of solution quality as compared with multiple-GA and due-date first approach.