Combinatorial strategies have received a lot of attention lately as a result of their diverse applications in areas of research, particularly in software engineering. In its simple form, a combinatorial strategy can reduce several input parameters (configurations) of a system into a small set of these parameters based on their interaction (combination). However, in practice, the input configurations of software systems are subjected to constraints, especially highly configurable systems. To implement this feature within a strategy, many difficulties arise for construction. While there are many combinatorial interaction testing strategies nowadays, few of them support constraints. This paper presents a new strategy, called Octopus to construct a combinatorial interaction test suites with the presence of constraints. The design and algorithms are provided in the paper in detail. The strategy is inspired by the behaviour of octopus to search for the optimal solution using multi-threading mechanism. To overcome the multi judgement criteria for an optimal solution, the multi-objective particle swarm optimisation is used. The strategy and its algorithms are evaluated extensively using different benchmarks and comparisons. The evaluation results showed the efficiency of each algorithm in the strategy. The benchmarking results also showed that Octopus can generate test suites efficiently as compared to state-of-the-art strategies.