Effective and efficient reasoning in adversarial environments is important for many real-world applications ranging from cybersecurity to military operations. Deliberative reasoning techniques, such as Automated Planning, often restrict to static environments where only an agent can make changes by its actions. On the other hand, such techniques are effective and can generate non-trivial solutions. To explicitly reason in environments with an active adversary such as zero-sum games, the game-theoretic framework such as the Double Oracle algorithm can be leveraged. In this paper, we leverage the notions of critical and adversary actions, where critical actions should be applied before the adversary ones. We propose heuristics that provide a guidance for planners about what (critical) actions and in which order have to be applied in a good plan. We empirically evaluate our approach in terms of quality of generated strategies (by leveraging Double Oracle) and CPU time required to generated such strategies.