The Pareto front can provide valuable information on land-use planning decision by revealing the possible trade-offs among multiple, conflicting objectives. However, seeking the Pareto front of land-use allocation is much more difficult than finding a unique optimal solution, especially when dealing with large-area regions. This article proposes an improved artificial immune system for multi-objective land-use allocation (AIS-MOLA) to tackle this challenging task. The proposed AIS is equipped with three modified operators, namely (1) a heuristic hypermutation based on compromise programming, (2) a non-dominated neighbour-based proportional cloning and (3) a novel crossover operator that preserves connected patches. To validate the proposed algorithm, it was applied in a hypothetical land allocation problem. Compared with the Pareto Simulated Annealing (PSA) method, AIS-MOLA can generate solutions more approximate to the Pareto front, with computation time amounting to only 5.1% of PSA. In addition, AIS-MOLA was also applied in the case study of Panyu, Guangdong, PR China, a large area with 389 × 337 cells. Experimental results indicate that this algorithm, even dealing with large-area land-use allocation problems, is capable of generating optimal alternative solutions approximate to the true Pareto front.Moreover, the distribution of these solutions can quantitatively demonstrate the complex trade-offs between the spatial suitability and the compactness in the study area. Software and supplementary materials are available at http://www.geosimulation.cn/AIS-MOLA/.