This paper presents and empirically studies an implementation framework for recommending the best suitable optimization algorithm based on landmarking features and meta-learning approaches. The landmarking abandons the traditional feature extraction techniques and/or approach. The landmarking features are obtained using the simplified algorithm on the problem and using only the relative performance of the algorithm as the feature dataset. On this basis, meta-learning approaches are applied to train the metamodel and make algorithm recommendations for new problems. In order to verify the effect, a set of multi-mode resource constrained project scheduling problem(MRCPSP) is selected as the objective. Four meta-heuristic algorithms, namely artificial bee colony, ant colony system, particle swarm optimization and tabu search, are selected as the recommended algorithms. The four meta-learning approaches, namely artificial neural network, k-nearest neighbourhood, decision tree and random forest, are used to generate the recommended meta-model. The empirical study shows that all the prediction results point to similar recommendation accuracy, with an average stabilised around 70% and a maximum at 95%. The optimization algorithm recommendation based on the landmarking and meta-learning approach is a new direction worthy of further exploration.