Code Smells are symptoms of poor design and implementation choices. Detect and identify Code Smell precisely provide guidance on software refactoring, and lead to improvement of software usability and reliability. Design problems of software systems could be quantified through Code Smell metrics. JavaScript has become one of the most widely used programming languages, class is a design pattern of JavaScript, loose coupling and strong cohesion are characteristics of a well-designed class. Prior works measured coupling and cohesion Code Smells of JS programs in lower levels, i.e., function-wide and statement-wide, which were capable for providing refactoring suggestions about basic implementations, but not enough to identify design problems. This paper proposed JS4C, a method to detect coupling and cohesion Code Smells of JS classes including FE, DC and Blob. This method is an approach of static analysis works on both server and client-side applications, it iterates over every class in software system and takes advantage of source code textual patterns. While JS4C detects Code Smells, it also determines intensity for each of them. Missing type information in static analysis is reinforced by extended object type inference and non-strict coupling dispersion (NSCDISP) metric during structural analysis. Experiments made on 6 open-sourced projects indicate that JS4C can correctly detect coupling and cohesion design problems.