Self-tracking has expanded exponentially in an era defined by the ubiquitous presence of wearable technologies and smart devices. From health and fitness to finances and productivity, these devices empower users to delve into their quantified self (QS) through an almost infinite amount of visualizations. However, a user has limited time to engage with the data, and the device with which they interact has limited screen space — this calls for the need to suggest the user proactively with valuable and informative plots. An algorithm can suggest and select plots of user activities and adapts to a user’s changing requirements while offering maximum usefulness in the information. We leverage combinatorial optimization to handle the multi-objective task of extracting the most informative 𝑘 plots from a much larger pool of 𝑁 plots. The novel optimization formulation encapsulates plot features into four unique objective functions designed to capture diverse aspects of data usefulness. We evaluate the efficacy of our selection method in silico for various diverse usage scenarios. The simulation results show that the proposed methodology is efficacious in realizing three objectives (‘relevance’, ‘freshness’, and ‘likability’) while identifying the need for refinement in the fourth objective (‘noteworthiness’). Our work demonstrates the design, development, and evaluation of a selection algorithm that delivers a relevant yet fresh selection of visualizations for a quantified-self user interested in keeping track of information related to several activities.