Crowdsourcing has already obtained a lot of attention from researchers due to the enormous power of solving complex problems in less time and at a minimal cost. Most of the research considers finding aggregated judgment from multiple diverse opinions of workers, and several algorithms such as majority-voting, weighted majority voting, and Expectation-Maximization (EM) algorithms are widely accepted. Recently, a relatively unexplored area of judgement analysis algorithm was introduced in the literature, and each crowd worker provides their opinions that comprise multiple components for a single question, and there is a constraint for each pair of components. However, in state-of-the-art approaches, the weight of the opinions is not considered. In this work, a new variant of the problem is introduced, while the weights between the components are considered. More importantly, the weights are not predefined, rather they are collected from the crowd workers. Thus, it can help us to explore many other solutions based on the users’ preferences which is very difficult to obtain from decision-makers if the crowdsourcing technique is not employed. Quantum computing has been a new approach to computer science that explores the idea of parallelism in computing. In this work, we have introduced a quantum genetic algorithm that explores solving the multiple objective-based weighted constrained crowd judgment analysis. In addition to that, we have introduced a new variant of constrained crowd judgment analysis that not only explores the solution to the multiple opinion-based solutions but also makes a better interpretation of the solutions by including textual information and additional weight parameters. The algorithm is applied to a real-life crowdsourced dataset obtained from the campus of the University of Kalyani. The experimental results demonstrate the applicability of the proposed method while providing a better solution compared to the traditional method.