Planning problems with continuous search and action spaces are common in real-world applications such as robotics or space exploration, whereas most planning algorithms rely on discrete features. Some attempts have been made to handle the continuous aspect especially in Reinforcement Learning, leveraging a Markov Decision Process modeling to handle it. Monte Carlo Tree Search-based methods also rely on this representation but still haven't made full use of some of the variants' potential, which is why we propose Continuous Nested Monte Carlo Search and Continuous Nested Rollout Policy Adaptation and show that they provide promising results on three problems: Map Traveling, Mountain Car Continuous and two instances of Chemical Processes from the pc-gym package.