The Storage Location Assignment Problem (SLAP) is a significant optimisation problem in warehouse management. Given a number of products, each with a set of items with different popularities (probabilities of being ordered), SLAP is to find the best locations for the items of the products in the warehouse to minimise the warehouse operational cost. Specifically, the operational cost is the expected cost of picking the orders. Grouping constraints are included to take the practical considerations into account in the problem. That is, the items belonging to the same product are more desirable to be placed together. In this paper, the SLAP with Grouping Constraints (SLAP-GC) is investigated, and an efficient Restricted Neighbourhood Tabu Search (RNTS) algorithm is proposed to solving it. RNTS adopts the problem-specific search operators to maintain solution feasibility, and the tabu list to prevent searching back and forth. RNTS was empirically compared with the mathematical programming method and a previously designed Genetic Programming method, which is demonstrated to be the state-of-the-art algorithm for SLAP-GC. The experimental results on the real-world data show that RNTS outperforms the state-of-the-art algorithms for SLAP-GC in terms of solution quality and speed. It managed to achieve optimal solutions for most of the small-scale instances much faster and outperformed the Genetic Programming method in terms of both solution quality and running time on all the test instances.