<p>Extracting valuable itemsets concerning towards high frequent and high utilization from a transactional database is a crucial objective in the field of data mining. This research examines both objectives namely frequency and utility of an itemset in a unified framework, using multi-objective optimization algorithm. More precisely, the activity of extracting frequent and high utility itemsets is formulated as a multi-objective problem. Subsequently, a novel algorithm namely Crowd distance based multi-objective binary Salp Swarm Optimization algorithm (CD-BSSOA) is proposed. This algorithm uses the crowding distance to compute the similarity between the solutions thus balances between exploration and exploitation. This algorithm has the capability to provide numerous itemset recommendations for decision-makers like market basket analysis, where identifying frequent and high-utility itemsets can help optimize product recommendations, enhance customer targeting, and improve inventory management. The algorithm was tested on a variety of real-world datasets including transactional data, classification datasets, and demographic datasets such as Chess, Connect, OnlineRetail, and Mushroom, representing different levels of complexity and item distributions. The interpreted results shows that the proposed algorithm has the efficiency to provide high number of non-dominant solutions on an average of 13.2% higher than the existing methods. In terms of hypervolume and coverage the proposed CD-BSSOA shows significant improvements with 9.2% and 11.6% respectively.</p>